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TRANSCRIPT
Sponsored by the International Society of Information Fusion
Fusion 2002 Final Program
8-11 July 2002
Loews Annapolis Hotel
Annapolis, MD (Washington DC Area), U.S.A.
Sponsored by ISIF and IEEE
www.fusion2002.org
Fusion 2002 Organizing Committee
General Chair:
X. Rong Li (University of New Orleans, USA)[email protected]
Advisory Chair:
Chee-Yee Chong (Booz Allen Hamilton, USA)[email protected], [email protected]
Steering Chair:
Dale Blair (Georgia Tech Research Institute, USA)[email protected]
Technical Program Co-Chairs:
Ben Slocumb (Numerica Corporation, USA)[email protected]
Thia Kirubarajan (McMaster University, Canada)[email protected]
International Organizing Committee Co-Chairs:
K. C. Chang (George Mason University, USA)[email protected]
Ruediger Dillman (Universitaet Karlsruhe (TH), Germany)[email protected]
Toshio Fukuda (Nagoya University, Japan)[email protected]
Exhibits Chair:
Amy Smith-Carroll (Naval Surface Warfare Center, USA)[email protected]
Finance Chair:
Dimitrios Charalampidis (University of New Orleans, USA)[email protected]
Liaisons Chair:
John Ackenhusen (Veridian, USA)[email protected]
Local Arrangements Chair:
Elizabeth McDaniel (Silver Bullet Solutions, USA)[email protected]
Publications Chair:
Robert Lynch (Naval Undersea Warfare Center, USA)[email protected]
Publicity Co-Chairs:
Neil Gordon (QinetiQ, UK)[email protected]
Subhash Challa (University of Melbourne, Australia)[email protected]
Registration Chair:
Quang Lam (Swales Aerospace, USA)[email protected]
Tutorial Chair:
Chun Yang (Sigtem Technology, Inc., USA)[email protected]
Sponsors Program Chair:
Nageswara Rao (Oak Ridge National Laboratory, USA)[email protected]
Web Site Administrators:
Andy Register (Georgia Tech Research Institute, USA)[email protected]
Tammy Williams (Georgia Tech Research Institute, USA)[email protected]
Table of Contents
General Chair’s Invitation ................................................................................. 1
ISIF President’s Welcome Message ................................................................. 2
Message from the Technical Program Co-Chairs ........................................... 3
Registration/General Information .................................................................. 4-9
Plenary Talks ............................................................................................... 10-12
The Information Fusion Challenge in the New World Order ....................... 10
Turbo Fusion ................................................................................................ 11
Visual Appearance Modeling and Perception with Retinal and
Cortical Signal Processing .......................................................................... 12
Schedule of Oral Presentations ................................................................. 13-24
Monday, 8 July 2002 Morning Presentations ......................................... 13-14
Monday, 8 July 2002 Afternoon Presentations ....................................... 15-16
Tuesday, 9 July 2002 Morning Presentations ........................................ 17-18
Tuesday, 9 July 2002 Afternoon Presentations ...................................... 19-20
Wednesday, 10 July 2002 Morning Presentations ................................. 21-22
Wednesday, 10 July 2002 Afternoon Presentations ............................... 23-24
Poster Session ............................................................................................ 25-26
Student Poster Session ................................................................................... 27
Invited Sessions ............................................................................................... 28
Tutorial Schedule: Thursday, 11 July 2002 .............................................. 29-39
TA1: A Taste of Multi-Sensor Data Fusion ................................................... 30
TA2: “Statistics 101” for Multisource-Multitarget Problems ......................... 31
TB1: Using Belief Function for Data Fusion ................................................ 32
TB2: Stochastic Optimization and the Simultaneous
Perturbation Algorithm ................................................................................ 33
TC1: Fusion of Multiple Classifiers ............................................................. 34
TC2: Data Fusion and Resource Management .......................................... 35
TD1 (part 1): Particle Filters for Sequential Bayesian Inference ................. 36
TD1 (part 2): Likelihood Ratio Detection and Tracking ............................... 37
TD2: Fundamentals of Information Fusion and Applications ....................... 38
TE1&2: Multitarget Tracking and Multisensor Fusion ................................. 39
Conference Center Layout .................................................... inside back cover
General Chair’s Invitation
The Fifth International Conference on
Information Fusion (Fusion 2002) will be held
Monday through Thursday, July 8 through 11 at
the Loews Annapolis Hotel, Annapolis,
Maryland, USA. Sponsored annually by the
International Society of Information Fusion, it is
the world’s largest and most comprehensive
technical conference devoted exclusively to
information fusion. On behalf of the Fusion
2002 Organizing Committee, it is my pleasure
to invite you to Fusion 2002. More than 300 people from all over the world are
expected to attend.
As a result of a record number of submissions this year, the Program Committee
has assembled a strong program, covering a broad spectrum of important topics
in information fusion. A total of 231 papers will be presented in 48 oral sessions,
a special student paper session, and a poster session.
The Conference will feature three plenary talks. Dr. Richard P. Wishner, Director
of Defense Advanced Research Projects Agency (DARPA) Information
Exploitation Office, will speak on Monday, July 8 on the topic of “The Information
Fusion Challenge for the New World Order.” On Tuesday, July 9, Professor H.
Vincent Poor of Princeton University will present a talk entitled “Turbo Fusion.”
Professor Bijoy K. Ghosh of Washington University in St. Louis will give his talk
entitled “Visual Appearance Modeling and Perception with Retinal and Cortical
Signal Processing” on Wednesday, July 10.
This is the first time in history that a full array of tutorials will be offered at a
Fusion conference. Ten tutorials will be presented by several world’s leading
experts, ranging from introductory presentations to in-depth coverage on a
variety of information fusion theories and applications.
Two other new features of the Conference this year will be a poster session and
a student paper program, which includes more than a dozen of papers whose
principal authors are students. A best student paper award will be given at the
Conference.
Thanks to the record level of financial and technical sponsorship from various
organizations and U.S. Government agencies, including IEEE Aerospace and
Electronic Systems Society, the Organizing Committee through hard work has
created an environment for the participants to benefit greatly through the
Conference. Given that Fusion 2000 and Fusion 2001 were held in Paris and
Montreal, respectively, and Fusion 2003 will be held in Australia, Fusion 2002 will
be the only Fusion conference held in the U.S. in a period of four or more years.
We hope that you will join us in Annapolis to make this conference a memorable
event, both scientifically and socially, possibly after you enjoy the Independence
(July 4) Holiday in Washington, DC.
X. Rong LiGeneral Chair
1
2
ISIF President’s Welcome Message
Dear Colleagues:
The Fifth International Conference on
Information Fusion (FUSION 2002) promises
to be another success, with a large number
of submissions and invited sessions. The
organizing committee has done an excellent
job and I look forward to seeing many of you
in Annapolis for FUSION 2002. I extend a
personal invitation for you to participate. I
look forward to the involvement of more people in the organization of the
conferences in future years.
I would like to thank the membership and the Board of Directors for entrusting me
again with the helm of ISIF. This is a privilege and an honor for me.
The success of our main activity, our annual FUSION conference, is witnessed by
the five proposals for organizing FUSION 2003. The proposal for Cairns, Australia
was accepted. We look forward to proposals for FUSION 2004 from the other
proponents for FUSION 2003. We believe that the continuity and success of the
FUSION conferences will be enhanced by active participation of the proposing
groups in previous years' conferences.
We are planning to update the ISIF website to give it a more professional
appearance. Among the updates will be the availability of benefits to members
that will include a 15% discount from Artech House and 20% from YBS Publishing
for their books. Members will use "ISIF" as identification for this purpose.
We are also planning to start an electronic journal "Advances in Information
Fusion." This will be discussed in more detail at the July board meeting.
Volunteers are solicited. They should contact Professor T. Kirubarajan at
We encourage you to make suggestions for any future activities.
See you in Annapolis,
Yaakov Bar-ShalomYY2K2P (Your Year 2K2 President)
An exceptional collection of papers was submitted to the Fifth International
Conference on Information Fusion, and we thank the authors for helping to make
this year’s technical program truly an outstanding one. We received 175 regular
papers and 83 invited session papers. This culminated in a program of 192 oral
papers, 25 poster papers, and 15 student poster papers. With such an
exceptional collection of submitted papers, we had to expand the technical
program: we increased the number of conference rooms from three to four and
reduced the presentation times from previous conferences. Such changes in the
program show significant growth and interest in the ISIF and the Fusion
Conference, and we truly appreciate this development especially in light of the
travel concerns lingering from the incidents of September 11, 2001.
As the Technical Program Co-Chairs, we appreciate the significant time
contributed by the members of the Technical Program Committee to review the
submitted papers. This year, the workload was significant due the large number
of papers submitted. We also would like to thank the organizers of invited
sessions for contributing many high-quality sessions on a number of pertinent
topics. Finally, the Co-Chairs would like to specifically acknowledge the
tremendous contributions of Andy Register and Tammy Williams who
administered the Fusion 2002 web site and the paper submission and review
software.
Thank you for participating in Fusion 2002. We look forward to your continued
support in the forthcoming years as well.
Benjamin Slocumb and T. Kirubarajan (Kiruba)Technical Program Co-Chairs
3
Message from the Technical Program Co-Chairs
Registration Information
On-site conference registration and check-in will be available:
• Sunday, July 7: 1700 to 2000
• Monday, July 8: 0700 to 1600
• Tuesday, July 9: 0700 to 1600
• Wednesday, July 10: 0700 to 1600
• Thursday, July 11: 0700 to 1400
General Information
Welcoming Reception:
A welcoming reception will be held in the Annapolis Atrium on Sunday evening
(7 July 2002) from 18:00 to 20:00. The registration desk will be open on Sunday
from 19:00 to 20:00.
Author's and Attendee's Breakfast
Monday, July 8, 2002
Author's Breakfast Windjammer Room 7:00-8:00 a.m.
Attendees' Breakfast Annapolis Atrium 7:00-8:00 a.m.
Student Poster Author’s Breakfast Weather Rail Lounge 7:00-8:00 a.m.
Tuesday, July 9, 2002
Author's Breakfast Windjammer Room 7:00-8:00 a.m.
Attendees' Breakfast Annapolis Atrium 7:00-8:00 a.m.
Poster Author's Breakfast Weather Rail Lounge 7:00-8:00 a.m.
Wednesday, July 10, 2002
Author's Breakfast Windjammer Room 7:00-8:00 a.m.
Attendees' Breakfast Annapolis Atrium 7:00-8:00 a.m.
Banquet
The conference banquet will be held at 7:00 p.m. on the second day
(Tuesday, July 9, 2002) in the Regatta Ballroom.
Hotel Location
Loews Annapolis Hotel
126 West Street
Annapolis, Maryland 21401
(410) 263-7777
Located in the heart of this historic port city, the hotel is within walking distance of
the area's numerous attractions. The hotel is only a 25-minute drive from both
Washington DC and Baltimore. The hotel provides a variety of tourist programs
including sightseeing of many attractions in Washington DC and Baltimore. There
are also a number of local attractions in the historic district of Annapolis, the historic
district of Annapolis, the Annapolis City Dock, Chesapeake Bay, and the U.S. Naval
Academy.
Driving Directions
• From Baltimore/Washington International (BWI)
• West to I-195
• Take the MD-170 rap towards Annapolis (I-97)
• Turn right onto Aviation Boulevard
• Turn left onto Dorsey Road
• Exit onto Aris T Allen Boulevard
• Stay straight to Forest Drive
• Turn slight left onto Hilltop Lane
• Turn left onto Spa Road
4
• Turn left onto Brown Street
• Turn right onto West Street
• Pass through one roundabout, remaining on West Street.
General Information (continued)
Hotel area map
5
General Information (continued)
Annapolis area map
More detailed driving directions and maps are available on the conference website
at www.fusion2002.org.
Northwest Annapolis
Northeast Annapolis
6
General Information (continued)Central Annapolis
Southwest Annapolis
Southeast Annapolis
7
General Information (continued)
Annapolis Attractions
Charters, Rentals & Boating Schools
• Admiral of the Bay, LLC
410-263-5196
• American Powerboat Schools and Charters
410-721-7517
• Annapolis Bay Charters
410-626-1223
• Annapolis City Marina
410-268-0660
Family Fun Sites
• Chesapeake Children's Museum
410-990-1993
• Horizon Organic Dairy Farm and Education Center
410-923-7600
• Smithsonian Environmental Research Center
301-261-4190
Family Fun Sites - Park Facilities
• Truxton Park
410-263-7958
Guided Tours/Sightseeing
• Annapolis Religious Heritage Tours
410-269-1737
• Annapolis Tours
410-263-5401
• Annapolis Walkabout
410-263-8253
• Naval Academy Guide Service
410-263-6933
• Discover Annapolis Tours
410-626-6000
• Heritage Tours
410-923-2771
• Historic Annapolis Foundation Walking Tour
410-268-5576
• Maryland State House Tours
410-974-3400
• Project Liberty Ship
410-558-0164
• Schooner Woodwind
410-263-7837
• Watermark Cruises
410-268-7600
Museums & Historic Sights
• Annapolis Maritime Museum
410-268-1802
• Anne Arundel County Historical Society
410-768-9518
• The Barracks
410-267-7619
• Benson Hammond House
410-768-9518
• Eastport's Barge House Museum
410-268-1802
8
• Government House
410-974-3531
• Hammond-Harwood House
410-269-1714
• Historic Baldwin Hall
410-923-3438
• Historic London Town and Gardens
410-222-1919
Museums & Historic Sights - U.S. Naval Academy Sites
• Bancroft Hall
410-293-5001
• U.S. Naval Academy, Armel-Leftwich Visitor Center
410-263-6933
• Class of 1951 Gallery of Ships
410-263-6933
• U.S. Naval Academy Chapel
410-263-6933
• U.S. Naval Academy Public Affairs
410-293-2291
Sports & Recreation
• Amphibious Horizons Kayaking
410-267-8742
• Annapolis Amblers
410-757-7899
Sports & Recreation - Golfing
• Atlantic Golf at Queenstown Harbor, South River and Ridge
800-767-4837
• Dwight D. Eisenhower Golf Course
410-571-0973
• Renditions Golf Course
410-798-9798
Sports & Recreation - Spectator Sports
• U.S. Naval Academy Athletic Association
410-293-4955
Theater/Visual & Performing Arts
• 49 West Coffeehouse
410-626-9796
• Annapolis Opera
410-267-8135
• Annapolis Summer Garden Theatre, Inc.
410-268-9212
• Annapolis Symphony Orchestra
410-269-1132
• Ballet Theatre of Maryland
410-636-6597
• Chesapeake Music Hall
800-406-0306
Airport Shuttle
BWI SuperShuttle's door to door service is available to and from most homes,
offices, or hotels in the Washington D.C. area, and most locations in Montgomery
and Prince George's Counties, as well as Northern Virginia. To arrange service to
BWI, reservations must be made at least 24 hours in advance by calling 1-800-
BLUE-VAN (1-800-258-3826).
General Information (continued)
9
Plenary Talk
The Information Fusion Challenge in the New World OrderDr. Richard P. Wishner, Ph.D., Director of IXO
Dr. Richard P. Wishner was selected as Director of the newly
formed Information Exploitation Office (IXO) at DARPA in the
Fall of 2001. IXO is the primary focal point within DARPA
responsible for solving the sensor-to-shooter problem for mobile
and fixed surface targets in all environments. IXO will develop
sensor and information system technology and systems with
application to battle space awareness, targeting, command and control, and the
supporting infrastructure required to address land-based threats in a dynamic, closed-
loop process. As Director, Dr. Wishner is responsible for formulating and executing
the investment strategy for high-payoff, innovative research and development for
these focal areas.
Prior to this appointment, Dr. Wishner has supported private industry as a senior
consultant in business management, technology development, systems analysis,
and information processing. Dr. Wishner was with DARPA from 1994 to 1997. He
served as the Assistant Director for DARPA’s Information Systems Office. He was
responsible for successful development and demonstration of exploitation signal
and information processing technologies in sensors, exploitation and information
integration technology and systems. In early 1994, Dr. Wishner served as Assistant
Deputy Undersecretary of Defense (Advanced Technology) for Special Projects.
His responsibilities included senior management of projects in information processing
and simulation including the thrust area of global surveillance and communication
and of synthetic environments. From 1979 to 1991, Dr. Wishner served as President
and Chairman of the Board, Advanced Decision Systems (ADS). ADS was a private
company whose market focus was R&D and custom system delivery to the military
and civilian markets. Its technology focus was applied artificial intelligence and other
advanced information processing technologies. During his tenure, Dr. Wishner
founded three spinout companies. In 1991, ADS was acquired by Booz-Allen &
Hamilton, Inc. (BAH) and Dr Wishner continued serving as Vice President and Partner
of BAH until 1993.
Dr. Wishner has served on a number of US Government committees and technical
evaluation studies and on the boards of several companies. He is author of 33
technical papers and has given numerous invited talks at conferences.
Dr. Wishner received his Bachelor’s of Science (1956), Master’s (1957) and Ph.D.
(1960) all in Electrical Engineering and all from the University of Illinois.
10
Plenary Talk
Turbo FusionH. Vincent Poor, Princeton University
Turbo processing refers to a class of iterative methods in which multiple constituent
algorithms exchange soft information between iterations in order to make joint
inferences about the same underlying phenomenon. Typically, the constituent
algorithms act on disparate observations or constraints related to a decision to be
made, and thus the turbo procedure can be viewed as a technique for fusing and
improving their tentative decisions. Such algorithms have enjoyed considerable
success in digital communications applications in recent years. This talk will discuss
turbo processing in this context. In particular, the problem of turbo multiuser detection,
in which the constituent algorithms consists of a channel decoder and a multiuser
detector, will be used as a paradigm for describing this technique.
H. Vincent Poor is a professor of Electrical Engineering at
Princeton University, where he is involved in teaching and
research in statistical signal processing and related areas.
Before joining the Princeton faculty in 1990, he taught at the
University of Illinois for a number of years. He has also spent
two sabbatical leaves at Imperial College in London, and is an
adjunct staff member at the IDA Center for Communications Research in Princeton.
Dr. Poor is a member of the National Academy of Engineering, and is a Fellow of
the IEEE, the Institute of Mathematical Statistics, and the Optical Society of America.
He has received several awards for his teaching and research, including recently
the 2001 IEEE Graduate Teaching Award, and the 2002 Joint Paper Award of the
IEEE Communications and Information Theory Societies. Recently, he was named
a Fellow of the John Simon Guggenheim Foundation.
11
Plenary Talk
Visual Appearance Modeling and Perception
with Retinal and Cortical Signal ProcessingProfessor Bijoy Ghosh, Washington University in St. Louis, USA
This talk will focus on the problem of shape estimation using multiple views from a
land based mobile robot equipped with c.c.d. cameras and a laser range finder that
can compute the range of a target along a fixed horizontal plane. The talk would
survey the problem of shape estimation from optical flow of points, lines and algebraic
curves and emphasize the fusion of camera and range sensors. Inspired from
Neuroscience, the talk would also introduce the role of cortical flow to the problem
of encoding visual input signals and subsequently decoding these inputs using
maximum likelihood estimates. To end the talk, we would model the appearance of
an object using principal component analysis and argue the role of appearance
dynamics as an alternative to optical flow based algorithms.
Bijoy K. Ghosh (S'78-M'79-SM'90-F'00) received his B.Tech
and M.Tech degrees in Electrical and Electronics Engineering
from India in 1977 and 1979 respectively. In 1983, he received
his Ph.D. in Engineering from the Decision and Control Group
of the Division and Applied Sciences at the Harvard University,
Cambridge, USA. Since 1983, he has been a faculty member in
the Systems Science and Mathematics department at Washington University where
he is currently a Professor and directs the center for BioCybernetics and Intelligent
Systems. Bijoy's research interests are in Multivariable Control Theory, Machine
Vision, Robotic Manufacturing and BioSystems and Control. In 1988, Bijoy received
the American Automatic Control Council's Donald P. Eckman award in recognition
of his outstanding contributions in the field of Automatic Control. In 2000, Bijoy
became a Fellow of the IEEE, for fundamental contributions in Systems Theory
with applications to robust control, vision and multisensor fusion. In 1993, Bijoy had
been an UNDP consultant under the TOKTEN program and visited the Indian Institute
of Technology, Kharagpur, India. In 1997, Bijoy also received the Japan Society for
the Promotion of Science Invitation Fellowship for research in Japan and visited
Tokyo Denki University and Mechanical Engineering Laboratory, Tsukuba City,
Japan. He has also held short term visiting positions at Osaka University, Japan in
1992 and Tokyo Institute of Technology, Japan in 1995. Bijoy is a permanent visiting
professor at the Tokyo Denki University and in the spring of 2001 he visited the
Electrical Engineering Department at Yale University, New Haven, USA.
12
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Con
verg
ence
in E
volu
tiona
ry
Com
puta
tion
Dav
id S
tark
, Jam
es S
pall
Land
Use
and
Lan
d C
over
Cha
nge
Pre
dict
ion
With
The
The
ory
Of E
vide
nce:
A S
tudy
Cas
e in
an
Inte
nsiv
e A
gric
ultu
ral R
egio
n in
Fra
nce
Laur
ence
Hub
ert-
Moy
, Sam
uel C
orgn
e,G
rego
ire M
erci
er, B
asel
Sol
aim
an
8:00
-9:0
0
7:00
-8:0
0
10:0
0-10
:20
9:40
-10:
00
9:20
-9:4
0
10:2
0-10
:40
14
Lo
cati
on
Ses
sio
n
11:4
0-12
:00
11:2
0-11
:40
11:0
0-11
:20
12:0
0-12
:20
10:4
0-11
:00
Mul
ti-se
nsor
Mul
ti-ta
rget
Tra
ckin
g us
ing
Out
-of-
sequ
ence
Mea
sure
men
ts
Mah
endr
a M
allic
k, J
on K
rant
, Yaa
akov
Bar
-Sha
lom
[M1
] T
HO
MA
S P
OIN
T R
OO
M
[M1B
] E
stim
atio
n a
nd
Tra
ckin
g II
Cha
ir: M
arce
l Her
nand
ezC
o-C
hair:
Yvo
Boe
rs
Aut
omat
ed S
urve
illan
ce T
rack
Filt
er T
unin
g B
y
Ran
dom
ized
Alg
orith
ms
Yvo
Boe
rs, H
ans
Drie
ssen
Gen
eral
Tra
ckin
g P
erfo
rman
ce D
escr
iptio
n fo
r
Sys
tem
s of
Sen
sors
Ake
And
erss
on, T
hom
as R
. Kro
nham
n
A F
uzzy
App
roac
h fo
r T
rack
ing
of
Low
-Alti
tude
Tar
get i
n th
e P
rese
nce
of M
ultip
ath
Pro
paga
tion
Y. M
. Che
n, H
. C. H
uang
[M3B
] F
uzz
y L
og
ic II
Cha
ir: P
ierr
e Va
linC
o-C
hair:
Jam
es S
mith
[M4B
] F
usi
on
Ap
plic
atio
ns
II
Cha
ir: L
arry
Sto
neC
o-C
hair:
Per
Sve
nsso
n
[M2B
] D
istr
ibu
ted
Det
ecti
on
, Cla
ssif
icat
ion
, an
d
Rec
og
nit
ion
II
Cha
ir: A
lexa
nder
Tar
tako
vsky
[M2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[M
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[M
4]
PO
INT L
OO
KO
UT R
OO
M
Mul
ti-Ta
rget
Mis
s D
ista
nce
and
Its A
pplic
atio
ns
John
Hof
fman
, Ron
ald
Mah
ler
Fuz
zy s
tatis
tical
cla
ssifi
catio
n m
etho
d fo
r m
ultib
and
imag
e fu
sion
Mic
kael
Ger
mai
n, M
atth
ieu
Voor
ons,
Goz
e B
ertin
Bén
ié, J
ean-
Mar
c B
ouch
er
Rea
l-tim
e m
ulti-
mod
el in
terp
olat
ion
of
rang
e-va
ryin
g ac
oust
ic p
ropa
gatio
n
Dan
iel C
hin,
Alb
ert B
iond
o
On
optim
um d
istr
ibut
ed d
etec
tion
and
robu
stne
ss o
f
syst
em p
erfo
rman
ce
Min
g X
iang
, Cho
ngzh
ao H
an
Ran
king
by
AH
P: A
Rou
gh A
ppro
ach
S. S
. Ala
m, S
hrab
onti
Gho
shR
elat
ing
the
Aud
io-V
isua
l Eve
nts
Cau
sed
by M
ultip
le
Mov
emen
ts: I
n th
e C
ase
of E
ntire
Obj
ect M
ovem
ent
Jini
ji C
hen,
Tet
suya
Mat
sum
oto,
Tos
hiha
ru M
ukai
,Yo
shin
ori T
akeu
chi,
Hiro
aki K
udo
Opt
imiz
atio
n of
dis
trib
uted
det
ectio
n ne
twor
ks w
ith
tree
str
uctu
res
Min
g X
iang
, Cho
ngzh
ao H
an
App
roxi
mat
ing
fuzz
y m
easu
res
by h
iera
rchi
cally
deco
mpo
sabl
e on
es
Jose
p D
omin
go, V
icen
ç To
rra
Ada
ptin
g a
Com
mer
cial
Sim
ulat
ion
Fra
mew
ork
to th
e
Nee
ds o
f Inf
orm
atio
n F
usio
n R
esea
rch
Per
Sve
nsso
n, V
ahid
Moj
tahe
d, P
ontu
s H
oerli
ng
A n
ew a
ppro
ach
for
cred
ibili
stic
mul
ti-se
nsor
asso
ciat
ion
Dom
iniq
ue G
ruye
r, M
orga
n M
ange
as, C
yril
Roy
ere
Fuz
zy L
ogic
Res
ourc
e M
anag
er: M
ulti-
Age
nt F
uzzy
Rul
es, S
elf-
Org
aniz
atio
n an
d V
alid
atio
n
Jam
es S
mith
Fus
ion
of T
wo
Par
sers
for
a N
atur
al L
angu
age
Pro
cess
ing
Tool
kit
Ahm
ad R
ahm
an, H
assa
n A
lam
, Hua
Che
ng, P
aul
Llid
o, Y
ulia
Tar
niko
va
Co
ffee
Bre
ak (
Stu
den
t P
ost
er S
essi
on
in t
he
An
nap
olis
Atr
ium
)
15
Lu
nch
(S
tud
ent
Pap
er P
ost
er S
essi
on
in t
he
An
nap
olis
Atr
ium
)12
:20–
14:0
0
Lo
cati
on
Ses
sio
n
Rob
ust T
rack
ing
With
Coo
pera
tive
Par
alle
l
Con
trol
lers
Arie
Ber
man
, Jos
hua
Day
an
[M3C
] Im
age
Fu
sio
n/P
roce
ssin
g I
Cha
ir: E
rik B
lasc
hC
o-C
hair:
Cha
veli
Ram
esh
[M4C
] S
enso
r/D
ata
Fu
sio
n I
Cha
ir: J
ean
Dez
ert
Co-
Cha
ir: S
ean
Wel
lingt
on
[M1
] T
HO
MA
S P
OIN
T R
OO
M
[M2C
] C
lass
ific
atio
n I
Cha
ir: N
agi R
aoC
o-C
hair:
Rob
ert L
ynch
[M1C
] E
stim
atio
n a
nd
Tra
ckin
g II
I
Cha
ir: K
aout
har
Ben
ameu
rC
o-C
hair:
Ram
anar
ayan
an V
isw
anat
han
[M2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[M
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[M
4]
PO
INT L
OO
KO
UT R
OO
M
Ora
l P
rese
nta
tio
ns f
or
Mo
nd
ay, 8
Ju
ly 2
00
2
Aft
ern
oo
n S
essio
ns
Noi
se E
stim
atio
n fo
r S
tar
Trac
ker
Cal
ibra
tion
and
Enh
ance
d P
reci
sion
Atti
tude
Det
erm
inat
ion
Qua
ng L
am, C
raig
Woo
druf
f, S
andy
Ash
ton,
Dav
e M
artin
Per
form
ance
Res
ults
of R
ecog
nizi
ng V
ario
us C
lass
Type
s U
sing
Cla
ssifi
er D
ecis
ion
Fus
ion
Rob
ert L
ynch
Mer
ger
of O
cean
Col
or In
form
atio
n fr
om M
ultip
le
Sat
ellit
e M
issi
ons
unde
r th
e S
IMB
IOS
Pro
ject
Of fi
ce
Ew
a K
wia
tkow
ska-
Ain
swor
th, G
iulie
tta S
. Far
gion
Sen
sor
valid
atio
n an
d fu
sion
usi
ng th
e N
adar
aya-
Wat
son
stat
istic
al e
stim
ator
Sea
n W
ellin
gton
, Joh
n A
tkin
son,
Rus
s S
ion
Exp
erim
ents
on
Fus
ion
of In
divi
dual
s C
lass
ifier
s an
d
a S
et o
f Cla
ssifi
ers
Pie
rre
Valin
, Cla
ude
Trem
blay
Exp
erim
ents
in M
ultim
odal
ity Im
age
Cla
ssifi
catio
n
and
Dat
a F
usio
n
Aly
Far
ag, H
ani M
ahdi
, Ref
aat M
oham
ed
Des
ign
Opt
imiz
atio
n of
the
Nad
aray
a-W
atso
n fu
ser
usin
g a
gene
tic a
lgor
ithm
Sea
n W
ellin
gton
, Jon
atha
n V
ince
nt
Sea
rch
Gam
e fo
r a
Mov
ing
Targ
et w
ith D
ynam
ical
ly
Gen
erat
ed In
form
atio
ns
Fre
deric
Dam
brev
ille,
Jea
n- P
ierr
e Le
Cad
re
Fix
ed a
nd T
rain
ed C
ombi
ners
for
Fus
ion
of
Imba
lanc
ed P
atte
rn C
lass
ifier
s
Fab
io R
oli,
Gio
rgio
Fum
era,
Jos
ef K
ittle
r
Fus
ion
of X
ray
and
geo
met
rical
dat
a in
com
pute
d
tom
ogra
phy
for
non
dest
ruct
ive
test
ing
appl
icat
ions
Ali
Moh
amm
ad-D
jafa
ri
Vot
ing
Fus
ion
Ada
ptat
ion
for
Land
min
e D
etec
tion
Ray
Kac
elen
ga, D
ave
Eric
kson
, Dav
id P
alm
er
Sig
nal P
aram
eter
Est
imat
ion
Bas
ed o
n on
e-bi
t
Qua
ntiz
ed D
ata
from
Mul
tiple
Sen
sors
Ant
onio
s M
engo
ulis
, Ram
anar
ayan
an V
isw
anat
han,
Aja
y M
ahaj
an
Col
labo
rativ
e M
ulti-
Mod
ality
Tar
get C
lass
ifica
tion
in
Dis
trib
uted
Sen
sor
Net
wor
ks
Hai
rong
Qi,
Xia
olin
g W
ang,
S. S
ithar
ama
Iyen
gar
Fus
ion
Per
form
ance
Mea
sure
s an
d a
Lifti
ng w
avel
et
tran
sfor
m b
ased
alg
orith
m fo
r im
age
fusi
on
Cha
veli
Ram
esh,
Tha
chan
Ran
jith
A D
ata
Fus
ion
Alg
orith
m fo
r M
ultis
enso
r S
yste
ms
Yuri
Vers
hini
n
14:4
0-15
:00
14:2
0-14
:40
14:0
0-14
:20
15:0
0-15
:20
16
Lo
cati
on
Ses
sio
n
16:2
0-16
:40
16:0
0-16
:20
15:4
0-16
:00
16:4
0-17
:00
The
Dyn
amic
Rea
l Tim
e S
enso
rs c
alib
ratio
n
Per
form
ance
Eva
luat
ion
John
Sud
ano
[M1
] T
HO
MA
S P
OIN
T R
OO
M
[M1D
] S
enso
r R
egis
trat
ion
Cha
ir: W
olfg
ang
Koc
hC
o-C
hair:
Bra
nko
Ris
tic
Per
form
ance
Bou
nds
for
Sen
sor
Reg
istr
atio
n
Bra
nko
Ris
tic, N
icke
ns O
kello
, Hw
a-Tu
ng O
ng
Sen
sor
Reg
istr
atio
n U
sing
Airl
anes
Hw
a- T
ung
Ong
, Bra
nko
Ris
tic, M
artin
Oxe
nham
Mul
ti-ta
rget
-Mul
ti-pl
atfo
rm S
enso
r R
egis
trat
ion
in
Geo
detic
Coo
rdin
ates
I. T.
Li,
John
Geo
rgan
as
[M3D
] Im
age
Fu
sio
n/P
roce
ssin
g II
Cha
ir: P
er S
vens
son
Co-
Cha
ir: P
arha
m A
arab
i
[M4D
] S
enso
r/D
ata
Fu
sio
n II
Cha
ir: J
ean-
Pie
rre
Le C
adre
Co-
Cha
ir: M
uham
ed F
aroo
q
[M2D
] C
lass
ific
atio
n II
Cha
ir: P
ierr
e Va
linC
o-C
hair:
Fra
nk L
oren
z
[M2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[M
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[M
4]
PO
INT L
OO
KO
UT R
OO
M
Kno
wle
dge-
Bas
ed F
usio
n of
For
met
s: D
iscu
ssio
n of
an E
xam
ple
Fra
nk L
oren
z, J
oach
im B
ierm
ann
Mul
ti-C
hann
el T
ime-
Fre
quen
cy D
ata
Fus
ion
Par
ham
Aar
abi,
Gua
ngji
Shi
Gen
eric
Sof
twar
e A
rchi
tect
ure
for
Dev
elop
men
t of
Dat
a F
usio
n S
yste
ms
Juan
A. B
esad
a, J
esus
Gar
cia,
Jav
ier
de D
iego
,G
onza
lo d
e M
igue
l, Jo
se R
. Cas
ar
Fus
ing
Bin
ary
and
Con
tinuo
us O
utpu
t of M
ultip
le
Cla
ssifi
ers
Kai
Goe
bel,
Wei
zhon
g Ya
n
Tag-
Bas
ed V
isio
n: A
ssis
ting
3D S
cene
Ana
lysi
s w
ith
Rad
io-F
requ
ency
Tag
s
Mus
taph
a B
oukr
aa, S
hige
ru A
ndo
Bay
esia
n ap
proa
ch w
ith h
iera
rchi
cal M
arko
v
mod
elin
g fo
r da
ta fu
sion
in im
age
reco
nstr
uctio
n
appl
icat
ions
Ali
Moh
amm
ad-D
jafa
ri
Fus
ing
and
Filt
erin
g A
rrog
ant C
lass
ifier
s
Am
y M
agnu
s, M
ark
Oxl
eyR
ecog
nitio
n of
Gra
y C
hara
cter
usi
ng G
abor
Filt
ers
Pei
feng
Hu,
Yan
nan
Zha
o, J
iaqi
n W
ang,
Zeh
ong
Yang
Dat
a F
usio
n A
rchi
tect
ure
for
Mar
itim
e S
urve
illan
ce
Ahm
ed G
ad, M
uham
ed F
aroo
q
Fus
ion
of A
stro
nom
ical
Mul
tiban
d Im
ages
on
a
Mar
kovi
an q
uadt
ree
Chr
isto
phe
Col
let,
Mire
ille
Louy
s, J
ean-
Noe
l Pro
vost
,A
nais
Obe
rto
A G
ener
al-P
urpo
se P
latfo
rm fo
r 3-
D R
econ
stru
ctio
n
from
Seq
uenc
e of
Imag
es
Ahm
ed E
id, S
herif
Ras
had,
Aly
Far
ag
Neu
ral n
etw
orks
est
imat
ion
of tr
uck
stat
ic w
eigh
ts b
y
fusi
ng w
eigh
t- in
- m
otio
n da
ta
Mor
gan
Man
geas
, Seb
astie
n G
lase
r ,V
icto
r D
olsc
emas
colo
Co
ffee
Bre
ak (
Stu
den
t P
ost
er P
aper
Ses
sio
n in
th
e A
nn
apo
lis A
triu
m)
15:2
0-15
:40
17
Co
ffee
Bre
ak9:
00-9
:20
Lo
cati
on
Ses
sio
n
Seq
uent
ial M
onte
Car
lo T
rack
ing
Sch
emes
For
Man
euve
ring
Targ
ets
With
Pas
sive
Ran
ging
Will
iam
Mal
colm
, Arn
aud
Dou
cet,
Ste
ven
Zol
lo
[T4A
] A
FO
SR
Info
rmat
ion
Fu
sio
n In
itia
tive
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
ohn
Tang
ney
[T1
] T
HO
MA
S P
OIN
T R
OO
M
[T2A
] D
istr
ibu
ted
Tra
ckin
g a
nd
Fu
sio
n
Invi
ted
Ses
sion
: Che
e C
hong
, Jim
Llin
as[T
1A]
Par
ticl
e F
ilter
s an
d M
on
te C
arlo
Met
ho
ds
Cha
ir: S
ubha
sh C
halla
Co-
Cha
ir: J
ean-
Pie
rre
Le C
adre
[T2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[T
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[T
4]
PO
INT L
OO
KO
UT R
OO
M
Ora
l P
rese
nta
tio
ns f
or
Tu
esd
ay, 9
Ju
ly 2
00
2
Mo
rn
ing
Se
ssio
ns
Ple
nary
Tal
k: P
rofe
ssor
Vin
cent
Poo
r, P
rince
ton
Uni
vers
ity, U
SA
Top
ic: T
urbo
Fus
ion
Per
form
ance
ana
lysi
s of
two
sequ
entia
l Mon
te C
arlo
met
hods
and
pos
terio
r C
ram
er-R
ao b
onds
for
mul
ti-ta
rget
trac
king
Car
ine
Hue
, Jea
n-P
ierr
e Le
Cad
re, P
atric
k P
erez
Opt
imal
Lin
ear
Est
imat
ion
Fus
ion
— P
art V
:
Rel
atio
nshi
ps
X. R
ong
Li, K
eshu
Zha
ng, J
uan
Zha
o, Y
unm
in Z
hu
AF
OS
R P
rogr
ams
in H
ighe
r Le
vels
of I
nfor
mat
ion
Fus
ion
John
Tan
gne
Aut
hors
' Bre
akfa
st in
Win
djam
mer
Roo
m, P
oste
r P
aper
Aut
hors
' Bre
akfa
st in
the
Wea
ther
Rai
l Lou
nge,
Atte
ndee
s' B
reak
fast
in A
nnap
olis
Atr
ium
An
Info
rmat
ion-
The
oret
ic J
ustif
icat
ion
for
Cov
aria
nce
Inte
rsec
tion
and
Its G
ener
aliz
atio
n
Mic
hael
Hur
ley
Info
rmat
ion
Fus
ion
for
Imag
e A
naly
sis:
Geo
spat
ial
Fou
ndat
ions
for
Hig
her-
Leve
l Fus
ion
A. W
axm
an, D
. Fay
, B. R
hode
s, T
. McK
enna
, R. I
vey,
N. B
ombe
rger
, V. B
ykos
ki, G
. Car
pent
er
Par
ticle
Filt
erin
g fo
r M
ulti-
targ
et T
rack
ing
and
Sen
sor
Man
agem
ent
Arn
aud
Dou
cet,
Ba-
Ngu
Vo,
Chr
isto
phe
And
rieu,
Man
uel D
avy
MA
P T
rack
Fus
ion
Per
form
ance
Eva
luat
ion
K. C
. Cha
ng, Z
hi T
ian,
Sho
zo M
ori,
Che
e-Y e
e C
hong
Info
rmat
ion
Fus
ion
for
Nat
ural
and
Man
-Mad
e
Dis
aste
rs
Jam
es L
linas
Not
e on
The
Gen
erat
ion
of R
ando
m P
oint
s U
nifo
rmly
Dis
trib
uted
in H
yper
-elli
psoi
d
Hon
gyan
SU
N, M
uham
ed F
aroo
q
Net
wor
k-C
entr
ic M
ultip
le F
ram
e A
ssoc
iatio
n
Sui
hua
Lu, A
ubre
y B
. Poo
re, B
rian
J. S
ucho
mel
Fro
m D
ata
to A
ctio
nabl
e K
now
ledg
e an
d D
ecis
ion
Kat
ia S
ycar
a, M
icha
el L
ewis
8:00
-9:0
0
7:00
-8:0
0
10:0
0-10
:20
9:40
-10:
00
9:20
-9:4
0
10:2
0-10
:40
[T3A
] In
form
atio
n F
usi
on
Usi
ng
Bay
esia
n
Net
wo
rks
Invi
ted
Ses
sion
: Qia
ng J
i, C
arl L
oone
y
Effi
cien
t Inf
eren
ce fo
r M
ixed
Bay
esia
n N
etw
orks
K.C
. Cha
ng, Z
hi T
ian
Info
rmat
ion
Fus
ion
with
Bay
esia
n N
etw
orks
for
Mon
itorin
g H
uman
Fat
igue
Pei
-Lin
Lan
, Qia
ng J
i, C
arl G
. Loo
ney
Tru
stw
orth
y S
ituat
ion
Ass
essm
ent v
ia B
elie
f
Net
wor
ks
Sub
rata
Das
, Dav
id L
awle
ss
Boo
sted
Lea
rnin
g in
Dyn
amic
Bay
esia
n N
etw
orks
for
Mul
timod
al D
etec
tion
T. C
haod
hury
, A. P
entla
nd, J
. Reh
g, V
. Pav
lovi
c
18
Lo
cati
on
Ses
sio
n
11:4
0-12
:00
11:2
0-11
:40
11:0
0-11
:20
12:0
0-12
:20
10:4
0-11
:00
On
Pla
tform
-Bas
ed S
enso
r M
anag
emen
t
Dan
Str
ombe
rg, F
redr
ik L
antz
, Mar
ia A
nder
sson
[T1
] T
HO
MA
S P
OIN
T R
OO
M
[T1B
] R
eso
urc
e M
anag
emen
t
Cha
ir: K
aout
har
Ben
ameu
rC
o-C
hair:
Fre
dric
k La
ntz
Dis
trib
uted
Tra
ckin
g S
yste
ms
and
thei
r O
ptim
al
Infe
renc
e To
polo
gy
Pie
rre
Dod
in, V
ince
nt N
imie
r
A p
roje
ct d
ecis
ion
supp
ort s
yste
m b
ased
on
a
eluc
idat
ive
fusi
on s
yste
m
Abd
ella
h A
khar
raz,
Jac
ky M
ontm
ain,
Gill
es M
auris
Sto
chas
tic D
ynam
ic P
rogr
amm
ing
Bas
ed
App
roac
hes
to S
enso
r R
esou
rce
Man
agem
ent
Rob
ert W
ashb
urn,
Mic
hael
Sch
neid
er, J
ohn
Fox
[T3B
] In
form
atio
n F
usi
on
Usi
ng
Bay
esia
n
Net
wo
rks
Invi
ted
Ses
sion
: Qia
ng J
i, C
arl L
oone
y
[T4B
] A
FO
SR
Info
rmat
ion
Fu
sio
n In
itia
tive
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
ohn
Tang
ney
[T2B
] D
istr
ibu
ted
Tra
ckin
g a
nd
Fu
sio
n
Invi
ted
Ses
sion
: Che
e C
hong
, Jim
Llin
as
[T2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[T
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[T
4]
PO
INT L
OO
KO
UT R
OO
M
Tra
ckin
g in
Dec
entr
aliz
ed A
ir-G
roun
d S
ensi
ng
Net
wor
ks
Sal
ah S
ukka
rieh,
Hug
h D
urra
nt-W
hyte
,M
atth
ew R
idle
y, E
ric N
ettle
ton
Act
ive
Info
rmat
ion
Fus
ion
For
Dec
isio
n M
akin
g
Und
er U
ncer
tain
ty
Yong
mia
n Z
hang
, Qia
ng J
i, C
arl G
. Loo
ney
Mon
itorin
g an
d In
form
atio
n F
usio
n fo
r S
earc
h an
d
Res
cue
Ope
ratio
ns in
Lar
ge-S
cale
Dis
aste
rs
F. d
'Ago
stin
o, D
. Nar
di, G
. Gris
etti,
A. F
arin
elli,
L. Io
cchi
Larg
e S
cale
Sim
ulat
ion
of a
Dis
trib
uted
Tar
get
Tra
ckin
g S
yste
m
Jae-
Jun
Kim
, Tar
unra
j Sin
gh, J
ames
Llin
as
An
impr
oved
Bay
es fu
sion
alg
orith
m w
ith th
e P
arze
n
win
dow
met
hod
Gan
g W
ang,
Gan
-De
Zha
ng, H
ai Z
hao
Info
rmat
ion
Fus
ion:
A H
igh-
Leve
l Arc
hite
ctur
e
Ove
rvie
w
John
Sal
erno
Sca
labl
e D
istr
ibut
ed D
ata
Fus
ion
D. N
icho
lson
, C. M
. Llo
yd, S
. J. J
ulie
r , J.
K. U
hlm
ann
App
licat
ion
of A
dapt
ive
Obj
ect R
ecog
nitio
n A
ppro
ach
to A
eria
l Sur
veill
ance
Sun
g B
aik,
Pet
er P
acho
wic
z
Som
e C
ompu
tatio
nal A
ppro
ache
s fo
r S
ituat
ion
Ass
essm
ent a
nd Im
pact
Ass
essm
ent
Mic
hael
Hin
man
Coa
litio
ns fo
r D
istr
ibut
ed S
enso
r F
usio
n
Mic
hael
How
ard,
Dav
id P
ayto
n, R
egin
a E
stow
ski
Situ
atio
n A
sses
smen
t via
Bay
esia
n B
elie
f Net
wor
ks
Sub
rata
Das
, Rac
hel G
rey,
Pau
l Gon
salv
esA
Coo
pera
tive
Con
trol
Tes
tbed
Arc
hite
ctur
e fo
r S
mar
t
Loite
ring
Wea
pons
Rob
ert M
urph
ey, J
. K. O
'Nea
l
Co
ffee
Bre
ak
19
Lu
nch
(S
tud
ent
Pap
er P
ost
er A
war
ds
in t
he
Reg
atta
Bal
lro
om
)12
:20–
14:0
0
Lo
cati
on
Ses
sio
n
Per
form
ance
Enh
ance
men
t of t
he IM
M E
stim
atio
n by
Sm
ooth
ing
X. R
ong
Li, V
esse
lin P
. Jilk
ov, L
ei L
u
[T3C
] B
ayes
ian
Met
ho
ds
I
Cha
ir: R
ober
t Lyn
chC
o-C
hair:
Mik
e M
orel
li
[T4C
] A
FO
SR
Info
rmat
ion
Fu
sio
n In
itia
tive
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
ohn
Tang
ney
[T1
] T
HO
MA
S P
OIN
T R
OO
M
[T2C
] P
rob
abili
stic
Mu
lti-
Hyp
oth
esis
Tra
ckin
g
and
Rel
ated
Met
ho
ds
Invi
ted
Ses
sion
: Tod
Lug
inbu
hl, P
eter
Will
ett
[T1C
] M
ult
iple
Mo
del
Tra
ckin
g I
Cha
ir: Y
aako
v B
ar-S
halo
mC
o-C
hair:
Hen
k B
lom
[T2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[T
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[T
4]
PO
INT L
OO
KO
UT R
OO
M
Ora
l P
rese
nta
tio
ns f
or
Tu
esd
ay, 9
Ju
ly 2
00
2
Aft
ern
oo
n S
essio
ns
A M
ultip
le M
odel
Mul
tiple
Hyp
othe
sis
Filt
er F
or
Sys
tem
s W
ith P
ossi
bly
Err
oneo
us M
easu
rem
ents
Yvo
Boe
rs, H
ans
Drie
ssen
An
Inte
grat
ed M
etho
d fo
r D
etec
tion,
Dat
a
Ass
ocia
tion
and
Tra
ckin
g of
Mul
tiple
Bro
adba
nd
Sig
nals
C. T
. Chr
isto
u
App
licat
ion
of D
emps
ter-
Sha
fer
The
ory
of E
vide
nce
to th
e C
orre
latio
n P
robl
em
Tony
DeS
imon
e, M
icha
el M
orel
li
Info
rmat
ion
Fus
ion
Tech
nolo
gy R
equi
rem
ents
for
the
Nat
iona
l Sys
tem
for
Geo
spat
ial I
ntel
ligen
ce (
NS
GI)
Ent
erpr
ise
Phi
l Hw
ang,
Chu
ng H
ye R
ead
A M
arko
v M
odel
for
Initi
atin
g Tr
acks
with
the
Pro
babi
litie
s M
ulti-
Hyp
othe
sis
Tra
cker
S. J
. Dav
ey, D
. A. G
ray,
S. B
. Col
egro
ve
Inve
rse
Pig
nist
ic P
roba
bilit
y Tr
ansf
orm
s
John
Sud
ano
Kod
ak M
ulti-
Sen
sor
IMIN
T F
usio
n D
ata
Col
lect
ion
Ale
x M
irzao
ffC
ombi
ning
IMM
and
JP
DA
for
trac
king
mul
tiple
man
euve
ring
targ
ets
in c
lutte
r
Hen
k B
lom
, Edw
in B
loem
Red
uced
Com
plex
ity S
patio
-Tem
pora
l Im
age
Bas
ed
Tra
ckin
g fo
r M
aneu
verin
g Ta
rget
s
V. K
rishn
amur
thy,
S. D
ey
Lear
ning
Bay
esia
n N
etw
orks
I -
A T
heor
y B
ased
On
MA
P-
MD
L C
riter
ia
Hep
ing
Pan
Exp
loiti
ng M
OD
TR
AN
Rad
iatio
n T
rans
port
for
Atm
osph
eric
Cor
rect
ion:
The
FLA
AS
H A
lgor
ithm
A. B
erk,
S. M
. Adl
er-G
olde
n, A
. J. R
atko
wsk
i, G
. W.
Fel
de, G
. P. A
nder
son,
M. L
. Hok
e, T
. Coo
ley,
J. H
.C
hetw
ynd,
J. A
. Gar
dner
, M. W
. Mat
thew
, L. S
.B
erns
tein
, P. K
. Ach
arya
, D. M
iller
, P. L
ewis
A d
-ste
p F
ixed
-lag
Sm
ooth
ing
Alg
orith
m fo
r
Mar
kovi
an S
witc
hing
Sys
tem
s
Qua
n P
an, Y
. Gan
g Ji
a, H
. Cai
zha
ng
Mul
ticom
pone
nt S
igna
l Cla
ssifi
catio
n us
ing
the
PM
HT
Alg
orith
m
P. A
insl
eigh
, T. L
ugin
buhl
Lear
ning
Bay
esia
n N
etw
orks
II -
A C
ompu
tatio
nal A
lgor
ithm
Hep
ing
Pan
Mul
tiple
-Ent
ity B
ayes
ian
Net
wor
ks fo
r S
ituat
ion
Ass
essm
ent
E. W
right
, S. M
ahon
ey, K
. Las
key,
M. T
akik
awa,
T.
Levi
tt
14:4
0-15
:00
14:2
0-14
:40
14:0
0-14
:20
15:0
0-15
:20
20
Lo
cati
on
Ses
sio
n
16:2
0-16
:40
16:0
0-16
:20
15:4
0-16
:00
16:4
0-17
:00
Tact
ical
Bal
listic
Mis
sile
Tra
ckin
g us
ing
the
Inte
ract
ing
Mul
tiple
Mod
el A
lgor
ithm
Rob
ert C
oope
rman
[T1
] T
HO
MA
S P
OIN
T R
OO
M
[T1D
] M
ult
iple
Mo
del
Tra
ckin
g II
Cha
ir: Y
vo B
oers
Co-
Cha
ir: R
ober
t Coo
perm
an
Fuz
zy M
ultip
le M
odel
Tra
ckin
g A
lgor
ithm
for
Man
euve
ring
Targ
et
Don
ggua
ng Z
uo, C
hong
Zha
o H
an, L
in Z
heng
,H
ongy
an Z
hu, H
ong
Han
A M
ulti-
mod
e Im
age
Tra
ckin
g S
yste
m B
ased
on
Dis
trib
uted
Fus
ion
Lin
Zhe
ng, C
hong
zhao
Han
, Don
ggua
ng Z
uo,
Hon
g H
an
[T3D
] B
ayes
ian
Met
ho
ds
II
Cha
ir: S
hozo
Mor
iC
o-C
hair:
Dav
id B
ello
t
[T4D
] A
FO
SR
Info
rmat
ion
Fu
sio
n In
itia
tive
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
ohn
Tang
ney
[T2D
] P
rob
abili
stic
Mu
lti-
Hyp
oth
esis
Tra
ckin
g
and
Rel
ated
Met
ho
ds
Invi
ted
Ses
sion
: Tod
Lug
inbu
hl, P
eter
Will
ett
[T2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[T
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[T
4]
PO
INT L
OO
KO
UT R
OO
M
The
Ped
estr
ian
PM
HT
M. E
fe, P
. Will
ett
A n
ew d
efin
ition
of q
ualif
ied
gain
in a
dat
a fu
sion
proc
ess:
app
licat
ion
to te
lem
edic
ine
Dav
id B
ello
t, A
nne
Boy
er, F
ranc
ois
Cha
rpill
et
Hyp
othe
sis
Man
agem
ent f
or In
form
atio
n F
usio
n
E. J
ones
, N. N
ikol
aos,
D. H
unte
r
Tra
ckin
g A
lgor
ithm
Spe
ed C
ompa
rison
s B
etw
een
MH
T a
nd P
MH
T
D. D
unha
m, R
. Dem
pste
r , S
. Bla
ckm
an
Uni
fied
Fus
ion
Sys
tem
Bas
ed O
n B
ayes
ian
Net
wor
ks F
or A
uton
omou
s M
obile
Rob
ots
Eva
Bes
ada-
Por
tas,
Jos
e A
nton
io L
opez
-Oro
zco,
Jesu
s M
anue
l de
la C
ruz
Age
nt-B
ased
Sen
sor
Fus
ion
& T
aski
ng fo
r IS
R
M. F
orre
ster
, J. O
rave
c, A
. Mik
lich,
M. H
offe
lder
,B
. But
eau
Tra
ckin
g in
Hyp
er-S
pect
ral D
ata
R. L
. Str
eit,
M. J
. Wal
sh, M
. L. G
raha
mD
istr
ibut
ed D
ata
Fus
ion
Usi
ng S
uppo
rt V
ecto
r
Mac
hine
s
Sub
hash
Cha
lla, M
arim
uttu
Pal
anis
wam
i,A
liste
r S
hilto
n
Mul
ti-A
gent
Dat
a F
usio
n: D
esig
n an
d Im
plem
enta
tion
Issu
es
Vla
dim
ir G
orod
etsk
i
Targ
et A
ssoc
iatio
n U
sing
Har
mon
ic F
requ
ency
Tra
cks
Har
y H
urd,
Tie
n P
ham
Situ
atio
n A
sses
smen
t Usi
ng G
raph
ical
Mod
els
Pet
er B
lado
n, R
icha
rd J
. Hal
l, W
. And
y W
right
Sec
ure,
Sel
f-or
gani
zing
Alli
ance
Mem
ory
for
Inte
rnet
Sea
rch
And
ras
Lorin
cz, E
otvo
s Lo
rand
Co
ffee
Bre
ak15
:20-
15:4
0
Ban
qu
et in
th
e R
egat
ta B
allr
oo
m
17:3
0-18
:30
19:0
0-21
:00
Po
ster
Ses
sio
n a
nd
Hap
py
Ho
ur
in t
he
An
nap
olis
Atr
ium
Mul
tiple
sen
sor-
Col
lisio
n av
oida
nce
syst
em fo
r
auto
mot
ive
appl
icat
ions
usi
ng a
n IM
M a
ppro
ach
for
obst
acle
trac
king
.
Ang
elos
Am
ditis
, Aris
tom
enis
Pol
ychr
onop
oulo
s,Io
anni
s K
aras
eita
nidi
s, G
eorg
e K
atso
ulis
,E
vang
elos
Bek
iaris
21
Co
ffee
Bre
ak9:
00-9
:20
Lo
cati
on
Ses
sio
n
Rob
ust R
epor
t Lev
el C
lust
er-t
o-T r
ack
Fus
ion
Joha
n S
chub
ert
[W3A
] In
form
atio
n F
usi
on
fo
r C
riti
cal
Infr
astr
uct
ure
Pro
tect
ion
Invi
ted
Ses
sion
: Jag
dish
Cha
ndra
, S. S
. Iye
ngar
,S
rikan
ta K
umar
[W4A
] Im
age
Fu
sio
n &
Exp
loit
atio
n
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
acqu
elin
e Le
Moi
gne
[W1
] T
HO
MA
S P
OIN
T R
OO
M
[W2A
] G
MT
I Tra
ckin
g
Invi
ted
Ses
sion
: Mah
endr
a M
allic
k
[W1A
] T
rack
Fu
sio
n I
Cha
ir: C
hee
Cho
ngC
o-C
hair:
Sub
hash
Cha
lla
[W2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[W
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[W
4]
WIN
DJA
MM
ER R
OO
M
Ora
l P
rese
nta
tio
ns f
or
We
dn
esd
ay, 1
0 J
uly
20
02
Mo
rn
ing
Se
ssio
ns
Ple
nary
Tal
k: P
rofe
ssor
Bijo
y G
hosh
, Was
hing
ton
Uni
vers
ity in
St.
Loui
s, U
SA
Top
ic: A
ppea
ranc
e M
odel
ing
and
Per
cept
ion
with
Ret
inal
and
Cor
tical
Sig
nal P
roce
ssin
g
Info
rmat
ion
fusi
on b
ased
on
fast
cov
aria
nce
inte
rsec
tion
filte
ring
Wol
fgan
g N
iehs
en
A V
aria
ble
Str
uctu
re M
ultip
le M
odel
Par
ticle
Filt
er fo
r
GM
TI T
rack
ing
M.S
. Aru
lam
pala
m, N
. J. G
ordo
n, M
. Ort
on, B
. Ris
tic
Min
imal
Sen
sor
Inte
grity
in S
enso
r G
rids
Raj
gopa
l Kan
nan,
Sud
ipta
Sar
angi
, S. S
. Iye
ngar
,S
ibab
rata
Ray
Evo
lvin
g F
eatu
re E
xtra
ctio
n A
lgor
ithm
s fo
r
Hyp
ersp
ectr
al a
nd F
used
Imag
ery
S. P
. Bru
mby
, P. A
. Pop
e, A
. E. G
albr
aith
J. J
. Szy
man
ski
Aut
hors
' Bre
akfa
st in
Win
djam
mer
Roo
m, A
ttend
ees'
Bre
akfa
st in
Ann
apol
is A
triu
m
Litto
ral T
rack
ing
usin
g P
artic
le F
ilter
Mah
endr
a M
allic
k, S
imon
Mas
kell,
Thi
a K
iruba
raja
n,N
eil G
ordo
n
Dyn
amic
I/O
Pow
er M
anag
emen
t in
Rea
l-tim
e
Sys
tem
s w
ith M
ultip
le-S
tate
I/O
Dev
ices
Vis
hnu
Sw
amin
atha
n, K
rishn
endu
Cha
krab
arty
Pro
gres
s in
Mul
tisen
sor,
Mul
tispe
ctra
l and
Hyp
ersp
ectr
al Im
age
Fus
ion
and
Min
ing
A. W
axm
an, D
. Fay
, B. R
hode
s, T
. McK
enna
, R. I
vey,
N. B
ombe
rger
, V. B
ykos
ki, O
. Par
sons
Fus
ion
unde
r U
nkno
wn
Cor
rela
tion
- C
ovar
ianc
e
Inte
rsec
tion
as a
Spe
cial
Cas
e
Ling
ji C
hen,
Pab
lo A
ram
bel,
Ram
an M
ehra
New
Ass
ignm
ent-
Bas
ed D
ata
Ass
ocia
tion
for
Tra
ckin
g M
ove-
Sto
p-M
ove
Targ
ets
Lin
Lin,
T. K
iruba
raja
n, Y
. Bar
-Sha
lom
Sup
port
for
Rel
iabi
lity
in S
elf-
Org
aniz
ing
Sen
sor
Net
wor
ks
Alv
in L
im
Exp
loita
tion
of L
AN
DS
AT
Imag
ery
and
Anc
illar
y D
ata
for
Bat
tlesp
ace
Cha
ract
eriz
atio
n
Set
h O
rloff,
Su
May
Hsu
, Hsi
ao-h
ua K
. Bur
ke
Tra
ck-t
o-Tr
ack
Fus
ion
for
Out
-of-
Seq
uenc
e Tr
acks
Sub
hash
Cha
lla, J
onat
han
Legg
SP
RT-
Bas
ed T
rack
Con
firm
atio
n an
d R
ejec
tion
X. R
ong
Li, N
ing
Li, V
esse
lin J
ilkov
Dep
ende
nce
In N
etw
ork
Rel
iabi
lity
Noz
er D
. Sin
gpur
wal
laM
ultip
le S
enso
r Im
age
Reg
istr
atio
n, Im
age
Fus
ion,
and
Dim
ensi
on R
educ
tion
of E
arth
Sci
ence
Imag
ery
J. L
e M
oign
e, A
. Col
e-R
hode
s, R
. Eas
tman
,T.
El-G
haza
wi,
K. J
ohns
on, S
. Kae
wpi
jit, N
. Lap
orte
,J.
Mor
iset
te, N
. Net
anya
hu, H
. Sto
ne, I
. Zav
orin
8:00
-9:0
0
7:00
-8:0
0
10:0
0-10
:20
9:40
-10:
00
9:20
-9:4
0
10:2
0-10
:40
22
Lo
cati
on
Ses
sio
n
11:4
0-12
:00
11:2
0-11
:40
11:0
0-11
:20
12:0
0-12
:20
10:4
0-11
:00
Tra
ckin
g an
d fu
sion
for
wire
less
sen
sor
netw
orks
Mar
cel H
erna
ndez
, Ala
n M
arrs
, Sim
on M
aske
ll,M
. R. O
rton[W
1]
TH
OM
AS P
OIN
T R
OO
M
[W1B
] T
rack
Fu
sio
n II
Cha
ir: B
rank
o R
istic
Co-
Cha
ir: J
ean
Dez
ert
Pla
nific
atio
n fo
r Te
rrai
n-A
ided
Nav
igat
ion
Seb
astie
n P
aris
, Jea
n-P
ierr
e Le
Cad
re
Bal
listic
Tra
ck In
itial
izat
ion
from
a B
isat
ellit
e
Sur
veill
ance
Imag
ing
Sys
tem
Jean
Dez
ert
Tem
pora
l Fus
ion
in M
ulti-
Sen
sor
Targ
et T
rack
ing
Sys
tem
Rui
xin
Niu
, Pra
mod
Var
shne
y, K
isha
n M
ehro
tra,
Chi
luku
ri M
ohan
[W3B
] In
form
atio
n F
usi
on
fo
r C
riti
cal
Infr
astr
uct
ure
Pro
tect
ion
Invi
ted
Ses
sion
: Jag
dish
Cha
ndra
, S. S
. Iye
ngar
,S
rikan
ta K
umar
[W4B
] Im
age
Fu
sio
n &
Exp
loit
atio
n
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
acqu
elin
e Le
Moi
gne
[W2B
] G
MT
I Tra
ckin
g
Invi
ted
Ses
sion
: Mah
endr
a M
allic
k
[W2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[W
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[W
4]
WIN
DJA
MM
ER R
OO
M
Info
rmat
ion
Fus
ion
Asp
ects
Rel
ated
to G
MT
I Con
voy
Tra
ckin
g
W. K
och
Mul
tisen
sor
Geo
regi
stra
tion
usin
g H
AR
T
(Hig
h A
ccur
acy
Reg
istr
atio
n Te
chni
que)
Der
ek L
ewis
Syn
chro
nize
d G
MT
I Rad
ar C
olle
ctio
n M
anag
emen
t i
a C
oalit
ion
Env
ironm
entQA
. J.
New
ma
The
Fed
erat
ion
of C
ritic
al In
fras
truc
ture
Info
rmat
ion
via
Pub
lish-
Sub
scrib
e E
nabl
ed M
ultis
enso
r D
ata
Fus
ion
Tim
Bas
s
Aut
omat
ed C
ontr
olle
d Im
ager
y C
aptu
re in
Urb
an
Env
ironm
ents
Set
h Te
ller
Dis
trib
uted
Mul
tirat
e In
tera
ctin
g M
ultip
le M
odel
(DM
RIM
M)
Filt
erin
g w
ith
Out
-of-
Seq
uenc
e G
MT
I Dat
a
L. H
ong,
S. C
ong,
D. W
icke
r
A C
ase-
Bas
ed R
easo
ner
for
Net
wor
k In
trus
ion
Det
ectio
n
Dan
iel S
chw
artz
, Sar
a S
toec
klin
, Erb
il Y
ilmaz
A S
elf-
Con
sist
ency
Tec
hniq
ue fo
r F
usin
g 3D
Info
rmat
ion
H. S
chul
tz, A
. Han
son,
E. R
isem
an, F
. Sto
lle, Z
. Zhu
,W
. Don
g-M
in
Sca
labl
e G
MT
I Tra
cker
Tho
mas
Kur
ien
An
Inte
ract
ing
Aut
omat
a M
odel
for
Net
wor
k
Pro
tect
ion
R. R
. Bro
oks,
J. M
. Zac
hary
, C. G
riffin
, N. O
rr
MO
SA
IC: A
Mod
el-B
ased
Cha
nge
Det
ectio
n P
roce
ss
Bria
n S
toss
el, S
hilo
h L.
Doc
ksta
der
Co
ffee
Bre
ak
A S
impl
e M
odel
for
Rel
iabl
e Q
uery
Rep
ortin
g in
Sen
sor
Net
wor
ks
Raj
gopa
l Kan
nan,
Sud
ipta
Sar
angi
, S. S
. Iy
enga
r
23
Lu
nch
12:2
0–14
:00
Lo
cati
on
Ses
sio
n
Con
text
-Bas
ed M
etho
ds fo
r Tr
ack
Ass
ocia
tion
Chr
istin
e P
ower
, Don
ald
Bro
wn
[W3C
] In
form
atio
n F
usi
on
Tec
hn
iqu
es f
or
Su
rvei
llan
ce a
nd
Sec
uri
ty A
pp
licat
ion
s
Invi
ted
Ses
sion
: Gia
nLuc
a F
ores
ti, P
ram
od V
arsh
ney
[W4C
] Im
age
Fu
sio
n &
Exp
loit
atio
n
Invi
ted
Ses
sion
: Alle
n W
axm
an, J
acqu
elin
e Le
Moi
gne
[W1
] T
HO
MA
S P
OIN
T R
OO
M
[W2C
] In
form
atio
n M
od
elin
g/L
earn
ing
I
Cha
ir: M
itch
Kok
arC
o-C
hair:
Nag
i Rao
[W1C
] D
ata
Ass
oci
atio
n I
Cha
ir: M
urat
Efe
Co-
Cha
ir: P
ierr
e B
lanc
-Ben
on
[W2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[W
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[W
4]
WIN
DJA
MM
ER R
OO
M
Ora
l P
rese
nta
tio
ns f
or
We
dn
esd
ay, 1
0 J
uly
20
02
Aft
ern
oo
n S
essio
ns
Join
t Int
egra
ted
Pro
babi
listic
Dat
a A
ssoc
iatio
n -
JIP
DA
Dar
ko M
usic
ki, R
ob E
vans
Req
uest
Man
agem
ent u
sing
Con
text
ual I
nfor
mat
ion
for
Cla
ssifi
catio
n
Mar
c C
onta
t, V
ince
nt N
imie
r, R
oger
Rey
naud
Impr
ovin
g P
erso
nal I
dent
ifica
tion
Acc
urac
y U
sing
Mul
tisen
sor
Fus
ion
for
Bui
ldin
g A
cces
s
Con
trol
App
licat
ions
L. O
sadc
iw, P
. Var
shne
y, K
. Vee
ram
ache
neni
Fus
ion
of M
ulti-
Mod
ality
Vol
umet
ric M
edic
al Im
ager
y
Mar
io A
guila
r
Nea
rest
Nei
ghbo
r P
roje
ctiv
e F
user
for
Fun
ctio
n
Est
imat
ion
Nag
esw
ara
Rao
A M
ultir
esol
utio
n O
utdo
or D
ual C
amer
a S
yste
m fo
r
Rob
ust V
ideo
-Eve
nt M
etad
ata
Ext
ract
ion
L.M
arce
naro
, L.M
arch
esot
ti, C
.Reg
azzo
ni
Mul
ti-M
odal
ity G
aze-
Con
tinge
nt D
ispl
ays
for
Imag
e
Fus
ion
S. N
ikol
ov, D
. Bul
l, C
. Can
agar
ajah
, M. J
ones
,I.
Gilc
hris
t
A C
ompa
rison
of D
ata
Ass
ocia
tion
Tech
niqu
es fo
r
Targ
et T
rack
ing
in C
lutte
r
Ahm
ed G
ad, F
. Maj
di, M
. Far
ooq
Cre
atin
g K
now
ledg
e fr
om H
eter
ogen
eous
Dat
a
Sto
ve P
ipes
Lisa
Sok
ol
Aut
omat
ed R
egis
trat
ion
of S
urve
illan
ce D
ata
for
Mul
ti-C
amer
a F
usio
n
P. R
emag
nino
, G. A
. Jon
es
Inte
rfer
omet
ric Im
age
Fus
ion:
Inte
rfer
omet
ry in
Spa
ce
R. L
yon,
J. D
orba
nd, G
. Sol
yar ,
U. R
anaw
ake
Ass
ocia
tion
of N
arro
wba
nd S
ourc
es in
Pas
sive
Son
ar
Pie
rre
Bla
nc-B
enon
, Den
is P
illon
Mul
ti-H
ypot
hesi
s D
atab
ase
for
Larg
e-S
cale
Dat
a
Fus
ion
Dav
id M
cDan
iel
Fus
ion
of C
olor
ed V
isua
l and
IR Im
ages
for
Con
ceal
ed W
eapo
n D
etec
tion
Yin
gli L
i, Z
hiyu
n X
ue, R
.S. B
lum
Dis
cuss
ion
Per
iod
Alle
n W
axm
an
14:4
0-15
:00
14:2
0-14
:40
14:0
0-14
:20
15:0
0-15
:20
24
Lo
cati
on
Ses
sio
n
16:2
0-16
:40
16:0
0-16
:20
15:4
0-16
:00
16:4
0-17
:00
Dat
a A
ssoc
iatio
n in
Clu
tter
with
an
Ada
ptiv
e F
ilter
Mur
at E
fe, D
omin
ique
Bon
vin,
Pie
rre
Bro
g
[W1D
] D
ata
Ass
oci
atio
n II
Cha
ir: K
uo-C
hu C
hang
Co-
Cha
ir: M
urat
Efe
Bay
esia
n A
ppro
ache
s to
Tra
ck E
xist
ance
- IP
DA
and
Ran
dom
Set
s
Sub
hash
Cha
lla, B
a-N
gu V
o, X
uzhi
Wan
g
A d
ynam
ic c
omm
unic
atio
n m
odel
for
loos
ely
coup
led
hybr
id tr
acki
ng s
yste
ms
Tho
mas
Sch
ardt
, Chu
nron
g Yu
an
Rad
ar D
etec
tion
Impr
ovem
ent b
y In
tegr
atio
n of
Mul
ti-O
bjec
t Tra
ckin
g
Ling
min
Men
g, W
olfg
ang
Grim
m, J
effr
ey D
onne
[W3D
] S
itu
atio
n A
nal
ysis
an
d S
itu
atio
nal
Aw
aren
ess
Invi
ted
Ses
sion
: Sté
phan
e P
arad
is, R
icha
rd B
reto
n
[W4D
] H
igh
Lev
el K
no
wle
dg
e B
ases
fo
r
Info
rmat
ion
Fu
sio
n
Invi
ted
Ses
sion
: Ray
Lui
zzi
[W2D
] In
form
atio
n M
od
elin
g/L
earn
ing
II
Cha
ir: E
rik B
lasc
hC
o-C
hair:
Gal
ina
Rog
ova
[W2
] W
IND
MIL
L P
OIN
T E
AS
T R
OO
M[W
3]
WIN
DM
ILL P
OIN
T W
ES
T R
OO
M[W
4]
WIN
DJA
MM
ER R
OO
M
Info
rmat
ion
Fus
ion
in a
Coo
pera
tive
Mul
ti-ag
ent W
eb
Info
rmat
ion
Ret
rieva
l
Kha
led
Sha
ban,
Otm
an B
asir,
Kha
led
Has
sane
in,
M. K
amel
Com
man
d D
ecis
ion
Sup
port
Inte
rfac
e (C
OD
SI)
for
Hum
an F
acto
rs a
nd D
ispl
ay C
once
pt V
alid
atio
n
R. B
reto
n, S
. Par
adis
, J. R
oy
Inte
llige
nt S
yste
ms
Tech
nolo
gy fo
r H
ighe
r Le
vel
Fus
ion
C. A
nken
, N. G
emel
li, P
. LaM
onic
a, R
. Min
eo, J
. Spi
na
Dis
trib
uted
rei
nfor
cem
ent l
earn
ing
for
sequ
entia
l
deci
sion
mak
ing
Gal
ina
Rog
ova,
Pet
er S
cott,
Car
los
Lolle
tt
Usi
ng O
ntol
ogie
s fo
r R
ecog
nitio
n: A
n E
xam
ple
M. K
okar
, J. W
ang
An
Ana
tom
ofun
ctio
nal B
rain
Kno
wle
dge
Ben
edic
te B
atra
ncou
rt, S
teph
ane
Bon
neva
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Poster Session
[P1] Model-Set Design for Multiple-Model Method - Part II: Examples
X. Rong Li, Zhanlue Zhao, Peng Zhang, Chen He
[P2] A Window Cumulative Normalized Distance based Global Optimization
Track Association Model
Jia-Zhou He, Guan-Hua Pan, Yan-Li Li, Qing Cai, Shi-Fu Chen
[P3] Comparison of plot and track fusion for naval sensor integration
John Miles, John Moon, Steven Symons
[P4] ALMA: A New Approach to Data Initial Association
Jia-Zhou He, Si-Peng Peng, Shi-Fu Chen, Zhi-Hua Zhou
[P5] Situation/Threat Assessment Fusion System (STAFS)
Jae Woo Joo, Dong Lae Cho, Jeung Won Choi
[P6] Ground Target Identification Fusion System
Jeung Won Choi, Dong Lae Cho, Jae Woo Joo
[P7] Automatic Video System for Aircraft Identification
Jose M Molina, Jess Garcia, Juan Besada, Javier Portillo, A. Berlanga
[P8] Towards a query assisted tool for situation assessment
Jurgen Fransson, Erland Jungert
[P9] A Set of Novel Textural Features Based on 3D Co-occurrence Matrix for
Content-based Image Retrieval
Tao Dacheng, Yuan Yuan, Liu Zhengkai, Yu Nenghai, Li Xuelon, Tang Xiao-ou
[P10] Confidence, Pedigree, and Security Classification for Improved Data
Fusion
Aaron Newman
[P11] MMW Collocated Detectors By Fusing Active and Passitive Detection
Yan Jin-Hai, Li Xing-Guo, Wang Ming
[P12] Sensor Fusion For Vehicle Health Monitoring and Degradation Detection
Qiao Sun
[P13] Bipolar Logic and Bipolar Knowledge Fusion
Wen-Ran Zhang
[P14] Scalable Data Fusion Using Astolabe
Robbert Van Renesse, Kenneth Birman, Werner Vogels
[P15] Identical Foundation of Probability Theory and Fuzzy Theory
Denis De Brucq, Olivier Colot, Arnaud Sombo
[P16] A Proposed System for Segmentation of Information Sources in Portals
and Search Engines Repositories
Ioannis Anagnostopoulos, Christos Anagnostopoulos, Eleftherios Kayafas, Vassili
Loumos, I. Papaleonidopoulos
[P17] User Based Data Fusion Approaches
Richard Akita
25
[P18] Fusing Cortex Transform and Intensity based Features for Image Texture
Classification
Md. Khayrul Bashar, Noboru Ohnishi
[P19] Neural-like growing networks in system of technical vision of robot
Vitaliy Yashchenko
[P20] High Performance Real-Time Fusion Architecture (HP-RTF)
Garry Fountain, Steve Drager
[P21] Characterization of the Optimum of a Quadratic Program with Convex
Constraints. Application to Sensor Data Fusion
Christian Musso, Pierre Dodin
[P22] Foreign Language Audio Information Management System
Marc Shichman, Mike Gaffney, Elizabeth Cornell Fake, Lisa Sokol
[P23] Monte Carlo-based filter for target tracking with a feature measurement
Donka Angelova, Boryana Vassileva, Tzvetan Semerdjiev
[P24] Fault Diagnosis Based On the Multiple Preset GFRF Models
Ruixuan Wei, Chongzhao Han, Xisheng Wang, Hongsen Yan
26
Poster Session (continued)
27
Student Poster Session
[S1] Tracking of Spawning Targets with Multiple Finite Resolution Sensors
Huimin Chen, T. Kirubarajan, Yaakov Bar-Shalom
[S2] Optimal Update with Out-of-Sequence Measurements for Distributed
Filtering
K.-S. Zhang, X. Rong Li, Yunmin Zhu
[S3] Optimal Linear Unbiased Filtering with Polar Measurements for Target
Tracking
Zhanlue Zhao, X. Rong Li, Vesselin Jilkov, Yunmin Zhu
[S4] A Modified Adaptive Track Fusion Approach
Qiao Xiangdon, Wang Baoshu
[S5] Multisensor data fusion architecture based on adaptive Kalman filters
and fuzzy logic performance assessment
P. Jorge Escamilla-Ambrosio, Neil Mort
[S6] Optimal Bandwidth Assignment for Distributed Sequential Detection
Qi Cheng, Pramod K. Varshney, Kishan G. Mehrotra, and Chilukuri K. Mohan
[S7] A region-based multi-resolution image fusion algorithm
Gemma Piella
[S8] Isolated Vowel Recognition Using Linear Predictive Features and Neural
Network Classifier Fusion
Jeff Byorick, Ravi Ramachandran, Robi Polikar
[S9] Evaluation of Wavelet Transform Algorithms for multi-resolution image
fusion
Saim Muhammad, Monica Wachowicz, L. M. T. de Carvalho
[S10] Sensor Placement for Grid Coverage under Imprecise Detections
Santpal Dhillon, Krishnendu Chakrabarty, S. S. Iyengar
[S11] Dynamic I/O Power Management in Real-time Systems with Multiple-
State I/O Devices
Vishnu Swaminathan, Krishnendu Chakrabarty
[S12] Classification of Traditional Chinese Medicine by Nearest-Neighbour
Classifier and Genetic Algorithm
Zhang Lixin, Zhao Yannan, Cai Shaoqing, Yang Zehong, Liu Hongyu, Wang Jiaxin
[S13] Improved Joint Probabilistic Data Association Algorithm
Wang Ming-Hui, You Zhi-Sheng, Peng Ying-Ning
[S14] Fault-Tolerant Interval Estimation Fusion By Dempster-Shafer Theory
Baohua Li, Yunmin Zhu, X. Rong Li
[S15] Fusion of Expert Knowledge with data using belief functions: a case
study in waste water treatment
Sebastien Populaire, Thierry Denoeux, Albert Mpe A Guilikeng, Joëlle Blanc, Philippe
Ginestet
Invited Sessions
A number of high-quality invited sessions will appear during the Fusion 2002
conference. A list of the session titles, their organizers, and the corresponding session
number(s) in the oral paper agenda is shown in the table below.
Invited Session Title
Information Fusion for Critical
Infrastructure Protection
GMTI Tracking
AFOSR Information Fusion
Initiative
Image Fusion & Exploitation
High Level Knowledge Bases for
Information Fusion
Situation Analysis and Situational
Awareness
Distributed Tracking and Fusion
Distributed Detection,
Classification, and Recognition
Probabilistic Multi-Hypothesis
Tracking and Related Methods
Information Fusion Using
Bayesian Networks
Information Fusion Techniques for
Surveillance and Security
Applications
Organizers
Jagdish Chandra
S.S. Iyengar
Srikanta Kumar
Mahendra Mallick
Allen Waxman
John Tangney
Allen Waxman
Jacqueline LeMoigne
Raymond A. Liuzzi
Stéphane Paradis
Richard Breton
Chee-Yee Chong
James Llinas
Alexander Tartakovsky
Tod Luginbuhl
Peter Willett
Qiang Ji
Carl G. Looney
Gian Luca Foresti
Pramod Varshney
Session Number
W3A-W3B
W2A-W2B
T4A-T4D
W4A-W4C
W4D
W3D
T2A-T2B
M2A
T2C-T2D
T3A-T3B
W3C
28
Tutorial Schedule
Fusion 2002 Tutorial program will take place on Thursday, July 11, 2002.
The table below lists the planned tutorials, the presenters, and the schedule.
Outlines for each tutorial, contact information for the presenters, and their
biography information are provided on the web site (www.fusion2002.org).
Morning Session (8:00 a.m. - 12:00 noon)
TA1 A Taste of Multi-Sensor Data Fusion
David L. Hall, The Pennsylvania State UniversityLOCATION: THOMAS POINT ROOM EAST
TB1 Using Belief Function for Data Fusion: Theory and Application
Philippe Smets, Université Libre de Bruxelles, BelgiumThierry Denoeux, Université de Technologie de Compiègne, FranceLOCATION: THOMAS POINT ROOM WEST
TC1 Fusion of Multiple Classifiers
Fabio Roli, University of Cagliari, ItalyLOCATION: WINDMILL POINT ROOM EAST
TD1 Particle Filters for Sequential Bayesian Inference (3hrs)
Arnaud Doucet, Melbourne University, AustraliaSimon Maskell, QinetiQ, UKNeil Gordon, QinetiQ, UK
Likelihood Ratio Tracking and Detection (1 hr)
Larry Stone, Metron, Inc.LOCATION: WINDMILL POINT ROOM WEST
TE1 Multitarget Tracking and Multisensor Fusion (Part 1 of 2)*
Yaakov Bar-Shalom, University of ConnecticutLOCATION: POINT LOOKOUT ROOM
Afternoon Session (1:00 p.m. - 5:00 p.m.)
TA2 “Statistics 101” for Multisource-Multitarget Problems:
Motivations, Concepts, Procedures, and Applications
Ronald Mahler, Lockheed Martin Tactical SystemsLOCATION: THOMAS POINT ROOM EAST
TB2 Stochastic Optimization and the Simultaneous Perturbation Algorithm
James C. Spall, Johns Hopkins University, Applied Physics LaboratoryLOCATION: THOMAS POINT ROOM WEST
TC2 Data Fusion & Resource Management
Christopher Bowman, Data Fusion & Neural Networks ConsultingLOCATION: WINDMILL POINT ROOM EAST
TD2 Fundamentals of Information Fusion and Applications
Erik Blasch, AFRL/SNASLOCATION: WINDMILL POINT ROOM WEST
TE2 Multitarget Tracking and Multisensor Fusion (Part 2 of 2)*
Yaakov Bar-Shalom, University of ConnecticutLOCATION: POINT LOOKOUT ROOM
* Each attendee of this course will receive the textbook“Multitarget-Multisensor Tracking: Principles and Applications” for $75.00
(Regular Price $120.00) in addition to the registration fee.
29
Tutorial TA1
A Taste of Multi-Sensor Data FusionDavid L. Hall, The Pennsylvania State University
Multi-sensor data fusion seeks to combine information from multiple sensors and
sources to achieve inferences that are not feasible from a single sensor or source.
Historically, the Department of Defense (DoD) has invested enormous amounts of
funding for data fusion systems for applications such as automatic target recognition,
automated situation assessment, identification-friend-foe-neutral (IFFN) systems,
and for smart weapons. More recently, other applications such as condition
monitoring of machinery, automated plant management, and environmental
modeling have begun to use techniques from multi-sensor data fusion. Thus, an
extensive legacy exists including process models, numerous algorithms, evolving
tool kits, and systems engineering methodology (e.g., for system design and
algorithm selection). Of particular note is the Joint Directors of Laboratories (JDL)
Data Fusion Working Group process model. This hierarchical model identifies levels
of fusion process, types of fusion functions, and candidate algorithms for performing
fusion. This process model is used as an organizing concept for this tutorial. Despite
this legacy, however, there are a number of challenges remaining for data fusion,
especially at higher levels of inferences. This tutorial provides a broad overview of
multi-sensor data fusion including the following. Introduction to multi-sensor data
fusion (the JDL model and key concepts); Overview of system engineering issues;
Understanding sensor processing and sensor limitations; Level 1 Fusion: multi-
target tracking and attribute fusion (identification); Level 2 Fusion: automated
reasoning for situation assessment; Level 3 Fusion: development of alternative
hypotheses for threat assessment; Level 4 Fusion: monitoring and control of fusion
processes; The human in the loop: the role of humans in information fusion; An
assessment of the state of the art in data fusion.
Dr. David Hall has over 25 years of experience in industrial
and academic research environments. Dr. Hall is the
Associate Dean for Research and Graduate Studies for the
Penn State School of Information Sciences and Technology.
Prior to this appointment he was an Associate Director of the
Penn State Applied Research Laboratory. In this role he
directed an interdisciplinary team of 175 scientists and engineers in conducting
research in information science, navigation research, systems automation and
communications science. Dr. Hall has industrial experience including director of
independent research & development (IR&D) and manager of a software signal-
processing group at Raytheon Corporation (HRB Division), manager of the
navigation analysis section at the Computer Sciences Corporation, and staff
scientist at MIT Lincoln Laboratory. Dr. Hall is the author of over 180 technical
papers and several books Mathematical Techniques in Multisensor Data Fusion
(Artech House, Inc.), Lectures in Multisensor Data Fusion (Artech House, Inc.),
and co-editor of The Handbook of Multisensor Data Fusion (CRC Press, Inc.). He
is a senior member of IEEE and a member of the NASA Aeronautics and Space
Transportation Advisory Committee. Dr. Hall has lectured internationally on the
topics of multisensor data fusion, artificial intelligence, and research management
and technology forecasting. In 2001, Dr. Hall was honored as the recipient of the
Joseph Mignona Data Fusion Award (a national award presented to the individual
who has made significant contributions to the growth and field of data fusion).
30
Tutorial TA2
“Statistics 101” for Multisource-Multitarget Problems:
Motivations, Concepts, Procedures, and ApplicationsRonald Mahler, Ph.D., Staff Scientist
Lockheed Martin Tactical Systems, Eagan, MN, USA
Progress in single-sensor, single-target problems has been greatly aided by the
existence of a systematic, rigorous, and yet practical engineering statistics. One
might expect that the same would be true for multisensor-multitarget information
fusion. Surprisingly, this has not been the case, even though a comprehensive
statistical foundation for multi-object problems—point process theory—has been in
existence for decades. The primary purpose of this tutorial is to provide an overview
of finite-set statistics (FISST), the ''engineering friendly'' version of point process
theory that Dr. Mahler introduced in 1994. It is a half-day version of an invited two-
day tutorial given last February at the International Conference on Information,
Decision, and Control in Adelaide, Australia. FISST is engineering-friendly in that it
is geometric, and preserves the “Statistics 101” formalism that signal processing
engineers already understand. Its core is a multisource-multitarget differential and
integral calculus, based on the fact that belief-mass functions are the rigorous
multisensor-multitarget counterparts of probability-mass functions. One novel
consequence is that FISST encompasses expert-system approaches such as fuzzy
logic, the Dempster-Shafer theory, and rule-based inference. A second purpose of
the tutorial is to demonstrate the relevance of FISST to practical applications such
as robust INTELL multisource NCTI, multitarget tracking, and performance
evaluation. A third purpose is to address such few criticisms of FISST as there have
been. The optimality and simplicity of Bayesian methods can be taken for granted
only within the confines of standard applications addressed by standard textbooks.
This tutorial will show that when one ventures out of these confines—especially in
multitarget problems—complacency can lead to serious problems.
Dr. Mahler has a B.S. in Electrical Engineering from the
University of Minnesota and a Ph.D. in Mathematics from
Brandeis University, Waltham, MA. Since 1995 he has co-
authored or co-edited over three dozen papers (including five
journal papers), two books, one book chapter, and one
monograph. He is currently PI/PE for nine R&D contracts with
agencies such as USARO, AFOSR, U.S. Army MRDEC, MDA, and three different
sites of AFRL. He has been invited to serve on technology review workshops for
AFRL, ARO, DARPA, and MDA; and to speak at many conferences, universities,
and government laboratories including Harvard, Johns Hopkins, the IEEE
Conference on Decision & Control, and the U.S. Air Force Institute of Technology.
31
Tutorial TB1
Using Belief Function for Data Fusion:
Theory and ApplicationsProfessor Philippe Smets, Université Libre de Bruxelles, Belgium
Professor Thierry Denoeux, Université de Technologies de Compiegne, France
The tutorial will present an up to date version of the theory of belief functions for the
representation of uncertainty. It will focus on the concepts underlying the models
and its numerous tools useful for data fusion problems. It will survey several examples
of practical and successful applications of the method. The tutorial will consist of
four hour-long lectures: Lecture 1: The use of belief functions to represent uncertainty
(PhS); Lecture 2: Case-Based Diagnosis (ThD); Lecture 3: Model-Based Diagnosis
using the General Bayesian Theorem (PhS); and Lecture 4: Other Applications of
the TBM (ThD+PhS).
Professor Philippe Smets was the founder and director of
IRIDIA, the AI lab at the Université Libre de Bruxelles. He had
a M.D. and a Ph.D. and was professor of Medical Statistics. He
is now retired. He has been the coordinator of several major
European Research Projects (ESPRIT Program of the European
Union) dealing with the representation and management of
uncertainty. Since 1978, his research focuses on belief functions. He has developed
the Transferable Belief Model for the representation of quantified beliefs. Within
that model, he developed many new tools, widely extending the initial work of Shafer.
He is the author of about 150 papers and edited 10 books on the problem of the
representation of imprecision and uncertainty.
Professor Denoeux graduated in 1985 as an engineer from
the Ecole Nationale des Ponts et Chaussées in Paris, and
received a doctorate from the same institution in 1989. He is
currently a Full Professor with the Department of Information
Processing Engineering at the Université de Technologie de
Compiègne, France. He is the author of about 100 papers in
the areas of pattern recognition, data analysis and uncertainty representation. His
current research interests concern the management of imprecision and uncertainty
in statistical pattern recognition and information fusion.
No
picture
provided
32
Tutorial TB2
Stochastic Optimization and
the Simultaneous Perturbation AlgorithmJames C. Spall, John Hopkins University, Applied Physics Laboratory
There has recently been much interest in iterative optimization algorithms that rely
only on measurements of the objective function to be optimized, not on direct
measurements or calculation of the gradient of the objective function. The instructor
will discuss the "simultaneous perturbation stochastic approximation (SPSA)"
algorithm for optimization of multivariate systems. For purposes of contrast in this
course, brief discussion will also be included on other modern approaches such as
simulated annealing and genetic algorithms. SPSA has recently attracted
considerable attention in areas such as statistical parameter estimation, pattern
recognition, nonlinear regression, neural network training, adaptive feedback control,
and experimental design. The essential features of SPSA are its efficiency for
multivariate problems and its relative ease of implementation for practitioners; these
features result largely from the underlying simultaneous perturbation gradient
approximation that only requires two objective function measurements independent
of the number of parameters being optimized.
James C. Spall joined The Johns Hopkins University, Applied
Physics Laboratory in 1983 and was appointed to the Principal
Professional Staff (the highest of the three categories of professional
staff) in 1991. He also teaches in the Johns Hopkins School of
Engineering and is Chairman of the Applied and Computational
Mathematics Program. Dr. Spall has published extensively in the
engineering and statistics literature and is the author of the forthcoming (2003)
textbook Introduction to Stochastic Search and Optimization (Wiley). He has also
worked in applications areas such as defense systems and transportation systems.
Dr. Spall has won a number of research, publication, and presentation awards.
33
Tutorial TC1
Fusion of Multiple ClassifiersProfessor Fabio Roli, Ph.D., University of Cagliari, Italy
In the field of pattern recognition, fusion of multiple classifiers is currently used for
solving difficult recognition tasks and designing high performances systems. From
a theoretical viewpoint, fusion of multiple classifiers allows overcoming some
limitations of the classical approach to design a pattern recognition system that
focuses on the search of the best individual classifier. From a practical viewpoint,
the concept of multiple classifiers derives naturally from the context and requirements
of many applications. As an example, in applications dealing with multiple sensor
systems, theory of multiple classifiers fits well with the need of designing decision
fusion modules based on a variety of sensor types. This tutorial opens with the
genesis of multiple classifier systems and the main background concepts. Parts II
and III illustrate the theoretical foundations of classifier ensembles and present the
main methods and algorithms for designing multiple classifiers systems. Techniques
for creating multiple classifiers by manipulation of training data (bagging, boosting,
etc.), and input and output features (feature selection, noise injection, etc.) are
presented. Main methods for combining multiple classifiers are illustrated. Voting
methods, Bayesian methods, linear combiners, Borda counts, adaptive and trained
methods, etc. The tutorial closes with a critical review of the state of the art and an
overview of real applications. No previous knowledge of the tutorial topics is
assumed. However, some basic familiarity with pattern recognition theory is helpful.
Professor Fabio Roli obtained MS degree and Ph.D. degree in
electronic engineering from the University of Genoa, Italy, in 1988
and 1993, respectively. He was with the research group on Image
Processing and Understanding of the Dept. of Biophysical and
Electronic Engineering, University of Genoa, Italy, from 1988 to
1994. Since 1995, he is with the Dept. of Electrical and Electronic
Eng. of the University of Cagliari, Italy. He is full professor of computer engineering
and leads the research activities of the Dept. in the areas of pattern recognition and
computer vision. His main area of expertise is the development of pattern recognition
algorithms for real applications. In this field, Prof. Roli has published more than
eighty conferences and journal papers, he is member of the scientific committees
of relevant conferences in pattern recognition, and he regularly acts as reviewer for
international journals. He is associate editor of the Electronic Letters on Computer
Vision and Image Analysis. Prof. Roli current research activity is focused on the
theory and the applications of multiple classifier systems. He published several
journal, conference papers, and book chapters on the tutorial topics. He organized
and co-chaired the series of international workshops on Multiple Classifier Systems
(www.diee.unica.it/mcs). Prof. Roli is currently serving as guest editor of three journal
special issues on “Fusion of Multiple Classifiers”, and he is one of the lecturers of
the International School on Neural Networks, E.R. Caianiello, 2002 Summer School
on “Ensemble Methods for Learning Machines”.
34
Tutorial TC2
Data Fusion and Resource ManagementDr. Christopher Bowman, Data Fusion and Neural Networks
Providing affordable tools to achieve consistent situational awareness and support
coordination of resources to achieve the desired outcomes is becoming ever more
important as the volume of information and alternative responses increase. The
two processes that lie between the sources and resources to support the user are
data fusion and resource management (DF&RM). Data Fusion is the process of
combining data/information to estimate or predict the state of a situation. ResourceManagement is the process of planning/controlling response capabilities to meet
mission objectives. The lack of common engineering standards at the applications
layer for DF&RM systems has been a major impediment to integration and re-use
of available technology. Developing cost-effective reusable multi-source fusion and
multi-resource management system software requires a standard architecture to
provide the toolbox organization for the evolving fusion and management object-
oriented “pattern” tools at various levels of hierarchy and abstraction. This short
course describes the emerging standard DF&RM Dual Node Network (DNN)
Architecture to provide the component toolbox, interfaces, and system engineering
methodology. Many applications of the architecture to DF&RM problems are
portrayed and alternative DF&RM algorithms are compared and explained. The
DNN Architecture will be applied to student defined DF&RM problems.
Dr. Bowman has over 25 years experience in data fusion (DF)
and over 15 years in Neural Networks (NN) applied to tactical
avionics, intelligence, missile defense, and surveillance systems.
For these applications he has proffered diverse computational
techniques including Bayes Nets, possibilistic (fuzzy and
evidential), symbolic (rules and scripts), and nonlinear pattern
recognition. He has applied these on various computing architectures (e.g.,
distributed workstations and massively parallel NN (digital and analog pulse stream).
He developed the Data Fusion and Resource Management (DF&RM) Dual Node
Network architecture (i.e., components, relationships, and design guidelines) that
supports affordable DF&RM synthesis, as well as comparative analyses. He gained
his Ph.D. in Mathematics from the University of California and has over 50
publications. He has lead or been a member of numerous Fusion Technology
Roadmap panels.
35
Tutorial TD1 (Part 1)
Particle Filters for Sequential Bayesian InferenceArnaud Doucet, Melbourne University, Australia
Simon Maskell, QinetiQ, UK
Neil Gordon, Qinetiq, UK
The tutorial is composed of three major parts. Part 1 Bayesian Inference: Pprobability
as quantification of belief, Definition of a pdf, Marginalisation theorem, and Bayes
rule. Part 2 Sequential Bayesian Inference: Assumptions (Markov process/state
being a sufficient statistic of measurement), Problem statement (recursive estimation
of posterior) and why it is hard, Example models (dynamic and measurement),
Convergence issues (must explicitly forget errors not just estimate parameters),
Analytic integration, Monte Carlo integration, Example: volume of a hypersphere,
Analytic integration - Kalman filter, Near-analytic integration – EKF (stressing that
you have to approximate the models to be Gaussian), Quasi-Monte Carlo integration
– UKF, and Monte Carlo integration - Particle filter and resampling. Part 3 Particle
Filtering: historical overview of field, SIS framework, Regularisation /jitter /MCMC,
Good choice of proposal distribution, Algorithm optimization, Rao-Blackwellisation,
Example of fixed parameter estimation, Example of image demo with CA model
and ~500 particles (showing EKF not working and PF working when SNR gets "too
low"), Example of multitarget tracking.
Arnaud Doucet received a PhD degree in Electrical Engineering
from the University of Paris-XI Orsay in 1997. From 1998-2000, he
conducted research at the Signal Processing group of Cambridge
University, in the United Kingdom. He is currently a Senior Lecturer
at the Department of Electrical Engineering of Melbourne University,
Australia. His research interests include Bayesian statistics, dynamic
models and Monte Carlo methods.
Simon Maskell received his BA degree in Engineering and MEng in
Electronic and Information Sciences from Cambridge University
Engineering Department, CUED, both in 1999. He works in the
Pattern and Information Processing group at QinetiQ Ltd. In 2000,
he was awarded a Royal Commission for the Exhibition of 1851
Industrial Fellowship and as a result is currently a PhD student at
CUED. His research interested include Bayesian inference, signal processing,
tracking and data fusion with particular emphasis on the application of particle filters.
Neil Gordon obtained a BSc in Mathematics and Physics from
Nottingham University in 1988 and a PhD degree in statistics from
Imperial College, University of London in 1993. He is currently with
the Pattern and Information Processing group at QinetiQ Ltd. His
research interests include Bayesian estimation and sequential Monte
Carlo methods (aka particle filters) with a particular emphasis on
target tracking and missile guidance. He has co-edited, with A Doucet and JFG de
Freitas, Sequential Monte Carlo methods in practice (New York: Springer-Verlag).
No
picture
provided
36
Tutorial TD1 (part 2)
Likelihood Ratio Detection and Tracking Dr. Lawrence D. Stone, Chief Operating Officer, Metron, Inc.
Likelihood Ratio detection and Tracking (LRT) is based on an extension of Bayesian
single target tracking to the case where there is either one or no target present. LRT
unifies detection and tracking into one seamless process that allows both functions
to be performed simultaneously and optimally. The following topics are covered
Overview of LRT
Bayesian Tracking LRT vs Bayesian Tracking and Track-Before-Detect
Mathematical Model for LRT
Basic Assumptions and Relationships
Target Likelihood Ratios
Measurement Likelihood Ratios
Basic Recursions
Why Use LRT
Minimize Bayes’ Risk of Target Declaration
Declare Target Present at Specified Confidence Level
Neyman-Pearson Criterion for Target Declaration
LRT Examples
Simple Simulation
Periscope Detection
TENET Example
Dr. Stone is a co-author of Bayesian Multiple Target Tracking,published by Artech House. He was the technical and project
manager for the development of a multiple-target, nonlinear,
correlator-tracker, NodeStar, designed for use in the Navy’s
Integrated Underwater Surveillance System. He continues to
perform research in the area of non-linear data fusion.
In 1986, he produced the probability maps used by the Columbus America Discovery
Group to locate the S.S. Central America which sank in 1857, taking an estimated
400 million dollars of gold coins and bars to the ocean bottom one and one-half
miles below.
In 1999 Dr. Stone was elected to the National Academy of Engineering. The
Operations Research Society of America awarded the Lanchester Prize to his text,
Theory of Optimal Search, as the best work in operations research published in
1975. He has published numerous papers in search theory, taught the subject at
the Naval Postgraduate School, and has participated in many search operations.
He participated in the development of the Coast Guard's computerized search and
rescue planning program, CASP. During the 1968 search for the submarine Scorpion,he provided on-scene analysis assistance for six weeks.
Education: Ph.D., Mathematics, Purdue University
37
Tutorial TD2
Fundamentals of Information Fusion and ApplicationsErik Blasch, Ph.D., MBA, Air Force Research Laboratory/Sensor Directorate
The seminar is intended to briefly cover the general topics concerning data fusion
with emphasis on the United States military’s perspective to fusion research. The
tutorial is designed to give a perspective of fusion fundamentals from the standpoint
of the advantages and disadvantages of fusion in everyday examples and general
military interests. The presenter is familiar with many of the people in the fusion
community in on-going fusion research and has taught classes to highlight the
fundamental importance of a pragmatic perspective to sensor and data fusion to
meet user needs. The tutorial sets the stage for fusion needs for many applications
with emphasis on military needs. Fusion is a pragmatic solution to a dynamic and
complex world but its methodologies have to be applied correctly to establish an
understanding that extends the human’s sensing capabilities. While the tutorial is a
brief overview of the fundamentals, key papers, extra slides, MATLAB examples,
and other information on general directions for a military fusion system will be supplied
with a CD that provides information useful to the fusion engineer.
Erik Blasch is a fusion engineer and program manager at the US
Air Force's Research Laboratory (AFRL) Sensors Directorate in
Dayton, OH working on fusion, ATR, and tracking programs. He is
also an adjunct professor at Wright State University (WSU) teaching
and supervising students in the Electrical Engineering (EE)
Department and a Reserve Captain at the Air Force of Scientific
Research (AFOSR) consulting on fusion and semiconductor research. He holds
these degrees: Ph.D. Electrical Engineering (Eng) from WSU '99, Ph.D. Mechanical
Eng. (ME) (ABD from University of Wisconsin (UW); MS Psychology ('00), MS
Economics ('99), Masters in Business Administration ('98) and MSEE ('97) from
WSU; MS Industrial and Systems Eng./Health Science ('95) and MSME ('94) from
Georgia Tech (GT); BSME/Economics ('92) from the Massachusetts Institute of
Technology; and attended the UW Medical School. He has worked for Texas
Instruments. Mobile Chemical, Ford, Solectria, as a sensor design engineer and a
consultant for Blasch Education and Rehabilitation (BEAR). From 1992-1996, he
was a graduate research assistant in the GT aerial robotics, UW robotics and medical
school. From 1996-2000, he was an active duty Captain in the USAF at Wright Labs
at WPAFB conducting research on tracking, ATR, and learning control theory. From
1996-1998, he was an intern at the WPAFB hospital in the emergency and
neuroscience departments. He founded the International Society of Information
Fusion (ISIF) and is on the Board, and active in IEEE, SPIE, and ION. He has
served on the Data Fusion Interactive Group and a member of three Scientific
Advisory Boards (SAB). His interests are target tracking, sensor fusion, automatic
target recognition, practical sensing strategies, neuroscience, biologically-inspired
robotics, oncology, theology, and learning control. He has published 90 articles in
these topics and a reviewer for many journals. He has worked on the development
a number of number of engineering software programs, including MTI, HRR, SAR
target tracking and identification which has been implemented in a pilot interface,
group tracking methodologies, cane coverage for the blind, and numerous robotics
implementations.
38
Tutorial TE1&2
Multitarget Tracking and Multisensor FusionYaakov Bar-Shalom
Review of the Basic Techniques for Tracking: the Kalman, the Alpha-Beta (Gamma)
and the Extended Kalman filters, their capabilities and limitations. Debiased
consistent measurement conversion from polar to Cartesian that allows the use of
optimal linear filters in practical problems (implemented in the E-2C upgrade;
applicable to long-range AEW radars). Tracking Targets with Multiple Behavior-
Modes. Agile beam radar allocation. Solution with an adaptive revisit time selection
algorithm for minimum radar energy with the IMM estimator. Tracking in Clutter. The
Probabilistic Data Association filter (PDAF) Agile Beam Radar Allocation and ECM.
The real-time experiment with an Aegis SPY-1 and F-14s at Wallops. Air Traffic
Control Tracking. Fusion of primary and secondary radar data. IMM vs. KF on real
data (800 targets, from 5 FAA/JSS radars). Large-Scale Tracking of Ground Targets.
The Variable Structure IMM (VS-IMM) with topographic information and road
constraints for precision tracking of ground targets with airborne GMTI radars.
Evaluation of VSIMM vs. IMM and different depth assignment (optimization based
MHT) algorithms. GEOP (Geometric enhancement of precision) from multiple
(asynchronous) radar data fusion. Acquisition of LO Targets. The CRLB in the
presence of false measurements. The limit of extractable track information from
cluttered data: application to sonar, ESA radar and real EO data. Comparison with
the MHT.
Yaakov Bar-Shalom is Board of Trustees Distinguished
Professor in the Department of Electrical and Computer
Engineering and Director of the ESP (Estimation and Signal
Processing) Laboratory at the University of Connecticut. His
current research interests are in estimation theory and target
tracking and has published over 280 papers and book chapters
in these areas and in stochastic adaptive control. He co-authored and edited 7
books. He has been elected Fellow of IEEE for "contributions to the theory of
stochastic systems and of multitarget tracking." He has been consulting to various
companies and government agencies, and originated the series of Multitarget
Multisensor Tracking short courses offered via UCLA Extension, at Government
Laboratories, private companies and overseas. During 1976 and 1977 he served
as Associate Editor of the IEEE Transactions on Automatic Control and from 1978
to 1981 as Associate Editor of Automatica. He was Program and General Chairman
of several major IEEE conferences. He is a member of the Board of Directors of the
International Society of Information Fusion (1999--2004) and Y2K and Y2K2
President of ISIF. He is co-recipient of the M. Barry Carlton Award for the best paper
in the IEEE Transactions on Aerospace and Electronic Systems in 1995.
39
Notes
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FUSION is an annual conference aimed at scientists and engineers
working in all aspects of information and data fusion. Data and
Information fusion have become key technologies in many defense
and civilian applications drawing inspiration from diverse fields
including artificial intelligence, pattern recognition and statistical
estimation. This rapidly growing field has created a forum for the
presentation of the latest research and innovations: its theoretical
basis and its application to defense and civilian problems, via a
series of FUSION conferences beginning in 1998.
The FUSION 2003 conference will be held at Radisson Plaza Hotel,
Cairns, Queensland, Australia. The Great Barrier Reef is within an
hours sail of Cairns.
We invite scientists, engineers, students and other professionals
involved in all aspects of information fusion to participate in the
Sixth International Conference on Information Fusion, FUSION
2003, to be held on 8-11 July 2003 at Raddison Plaza Hotel, Cairns,
Australia and make it a great success. Fusion 2003 will include
regular contributed sessions, invited sessions, student paper
program, plenary talks, and tutorials.
IMPORTANT DATES
summary due: 15 November 2002
invited sessions due: 1 February 2003
submission of draft papers: 1 February 2003
notification of acceptance: 1 April 2003
camera ready papers due: 1 May 2003
Ongoing updates about this conference, including a call for papers,
can be found at www.fusion2003.org. The deadline for abstract
submission is 15 November 2002.