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1

2007

Ultra Wide Band Multipath Exploitation for Anchor-less Localization and Indoor Environment Characterization - V. La Tosa - 03/12/10

2010

Vincenzo La Tosa

Primary Supervisor: Pr. Bernard Uguen (Univ. Rennes 1)

Co-Supervisor: Dr. Benoît Denis (CEA-LETI)

Ultra Wide Band Multipath Exploitation for

Anchor-less Localization and Indoor

Environment Characterization



2

2007


Introduction

Problem Statement

State-of-the-art

Proposed Solutions with an UWB Coherent Receiver (CR)

Proposed Solution with an UWB Non-Coherent Receiver (NCR)

Conclusion & Perspectives

Summary



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2007


Introduction

Problem Statement

State-of-the-art




Summary



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2007


IR-UWB: what is it?

• Impulse Radio – Ultra Wide Band (IR-UWB) is a low-power and short rangecommunication technology using a bandwidth larger than 500 MHz

• pulses are very short in time (e.g. about 1 ns for a 1 GHz bandwidth), thanks to the very large bandwidth

Introduction (1/3)

0 10 20 30 40 50 60 70 80-1.5

-1

-0.5

0

0.5

1

1.5

Time [ns]

Receiv

ed t

ensio

n [

mV

]

Received signal, 802.15.4a channel #4, 5th interaction order allowed



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2007


IR-UWB: what is it?



Regulation :

• different for US (FCC) and EU (ECC)

• very low Power Density Spectrum (PSD) allowed by FCC: -41.3 dBm/MHz

Introduction (1/3)



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2007


IR-UWB: what is it?



Regulation :

• different for US (FCC) and EU (ECC)

• very low Power Density Spectrum (PSD) allowed by FCC: -41.3 dBm/MHz

IEEE 802.15.4a standard (2007):

• low data rate (LDR) and peer-to-peer ranging for Wireless Sensor Networks (WSN)

Introduction (1/3)



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Environment characterization: what is it?

• the characterization of a given indoor environment is the description of :

geometry (i.e. room map)

materials constituting environment

Introduction (2/3)



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Anchor-less localization: what is it?

• for a given radio device the capability of getting aware of its coordinates in a local and relative Cartesian coordinate system in the surrounding environment

• no node can be an anchor (i.e. reference node)

Introduction (3/3)

x

y

1x 2x

1y

2y



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Introduction

Problem Statement

State-of-the-art




Summary



10

2007


Joint environment characterization and anchor-less localizationwhile communicating

Problem statement



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2007



The two SISO nodes belonging to a WSN exchange packets

Problem statement



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Thanks to multi-path diversity, the receiver can estimate room dimensions and nodes coordinates in the room

Problem statement

Dx

Dy

1x 2x

1y

2y



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Thanks to multi-path diversity, the receiver can estimate room dimensions and nodes coordinates in the room

Application fields: robotics, home automation, home entertainment, mobility in dangerous environments

Problem statement

Dx

Dy

1x 2x

1y

2y

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Introduction

Problem Statement

State-of-the-art




Summary



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Environment characterization:

UWB Imaging (R. Zetik, 2005)

UWB monostatic radar technique or Single Input Multiple Output (SIMO) transmission (i.e. multiple receiving antennas) in a mobile scenario, providing an image of the environment and exploiting collected Channel Impulse Responses (CIR)

State-of-the-art



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UWB monostatic radar technique or Single Input Multiple Output (SIMO) transmission (i.e. multiple receiving antennas) in a mobile scenario, providing an image of the environment starting from collected Channel Impulse Responses (CIR)

Simultaneous Localization And Mapping (SLAM) (J. A. Castellanos,1997)

mobile robots discovering the indoor environment without radio communications (e.g. through camera, laser)

State-of-the-art



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2007







Joint communications, localization and environment characterization:

Indoor Mapping (W. Guo, 2005)

geometric relationships linking the differences of Times of Arrival with geometry

State-of-the-art



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2007







Joint communications, localization and environment characterization:

Indoor Mapping (W. Guo, 2005)

geometric relationships linking the differences of Times of Arrival with geometry

Radiolocation with anchors:

Different solutions (S. Gezici, 2005)

AoA, Signal Strength, ToA

State-of-the-art



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2007


Two different approaches:

Coherent Receiver (CR)

high frequency sampling (i.e. Nyquist compliant, fs = 15 GHz)

floating point dynamic range for signal representation

Described Investigations



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Two different approaches:

Coherent Receiver (CR)

high frequency sampling (i.e. Nyquist compliant, fs = 15 GHz)

floating point dynamic range for signal representation

Non-Coherent Receiver (NCR)

low frequency sampling (i.e. much lower than Nyquist frequency)

4-bits dynamic range for signal representation

Described Investigations



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Indoor environment model:

rectangular room with walls numbered anticlockwise

for the reflected paths, the label of parameters corresponds to that of the interacting wall

Angles of Incidence (AoIs) defined as the angle involved in the reflection

Common Environment Model

11,1,1 , , , rxtx



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11,1,1 , , , rxtx

,1tx



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11,1,1 , , , rxtx

,1rx



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11,1,1 , , , rxtx

22,2,2 , , , rxtx



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33,3,3 , , , rxtx

11,1,1 , , , rxtx

22,2,2 , , , rxtx



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11,1,1 , , , rxtx

22,2,2 , , , rxtx 44,4,4 , , , rxtx

33,3,3 , , , rxtx



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Introduction

Problem Statement

State-of-the-art




Summary



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Single path in the frequency domain:

generic received path:

Signal Model



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Signal Model



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Signal Model



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antennas filter the pulse feeding the transmitting antenna, depending on DoD ( ) and DoA ( ), in addition to path channel

Signal Model



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antennas filter the pulse feeding the transmitting antenna, depending on DoD ( ) and DoA ( ), in addition to path channel

Tension of received multipath signal in the time domain:

corresponds to the sum of received paths

with filtered white Gaussian noise and

Signal Model



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Principles:

a database of templates can be locally built thanks to prior antenna characterization and relying on the previous deterministic model

CR approach (1/2)



34

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Principles:


CR approach (1/2)

Templates for direct paths

and

single-bounce paths

Templates indexed

by an AoA/AoD

and/or AoI



35

2007


Principles:


a good representation (i.e. floating point) of the received signal is available

CR approach (1/2)

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]

Received tension only considering direct path and single-bounce paths



36

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Principles:


a good representation (i.e. floating point) of the received signal is available

the received multipath components are assumed to be detectable thanks to the intrinsic UWB time resolution (i.e. path-by-path approach)

CR approach (1/2)

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]




37

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Principles:

each received multipath component is associated to one or more templates through cross-correlation

each received multipath component is associated with a set of transmission parameters (i.e. indexing the local database of templates)

CR approach (2/2)

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Multipath component cross-corr



38

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Principles:

each received multipath component is associated to one or more templates through cross-correlation

each received multipath component is associated with a set of transmission parameters (i.e. indexing the local database of templates)

CR approach (2/2)

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Multipath component cross-corr Template(s)Transmission

parameters

Param a

Param b

ab



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Principles illustration:

CR approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Cross-correlation with the local database of predicted

waveforms

0 0 0, ,



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CR approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]



waveforms

0 0 0, ,



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CR approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


1 1 1 1, , ,


waveforms

0 0 0, ,

1 1 1 1, , , 1 ,1 ,1 1, , , tx rx

Detection order Model order



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2007



CR approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


1 1 1 1, , ,

????


waveforms

0 0 0, ,

Label mapping ambiguity



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The proposed algorithms aim at linking the available sets of transmission parameters to a couple of nodes coordinates and an environment geometry

CR approach

0 0 0, ,

1 1 1 1, , ,

2 2 2 2, , ,

3 3 3 3, , ,

4 4 4 4, , ,

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Dx

Dy

1x 2x

1y

2y



44

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The proposed algorithms aim at linking the available sets of transmission parameters to a couple of nodes coordinates and an environment geometry

The error on and depends on the antenna radiation pattern

CR approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Dx

Dy

1x 2x

1y

2y

0 0 0, ,

1 1 1 1, , ,

2 2 2 2, , ,

3 3 3 3, , ,

4 4 4 4, , ,



45

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Tree algorithm for the CR case (1/3)

Transmission parameters (i.e AoIs, AoAs, AoDs and ToAs) are considered as measurements, related to an hidden state

• nodes coordinates and room main dimension are the hidden state variables

Dx

Dy

1x 2x

1y

2y

0 0 0, ,

1 1 1 1, , ,

2 2 2 2, , ,

3 3 3 3, , ,

4 4 4 4, , ,

Parameters measurementsHidden state (i.e. coordinates

and room dimension)



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Dx

Dy

1x 2x

1y

2y

0 0 0, ,

1 1 1 1, , ,

2 2 2 2, , ,

3 3 3 3, , ,

4 4 4 4, , ,

Parameters measurements Estimation treeHidden state (i.e. coordinates

and room dimension)

Likelihood-oriented

…

…

a

b

a

c

b

d

e

d

f

e

a

c

b

… b

a

c

d



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For instance, the true value of the AoI labeled as 1, according to the wall numbering in the model, is bound up with nodes coordinates as follows:

and the true distance between nodes is simply:



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For instance, the true value of the AoI labeled as 1, according to the wall numbering in the model, is bound up with nodes coordinates as follows:

and the true distance between nodes is simply:

The solution detailed in the following does not exploit AoAs and AoDs but only AoIs and ToAs (i.e. MAL, basic algorithm)



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Assuming a Gaussian centered noise affecting measurements, a likelihood function can be easily built for each path label mapping

All the configurations are equally probable a priori 4

41



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The path label mapping ambiguity is solved using Averaged Likelihood Functions (ALFs) ALFs are built as mixtures of likelihood functions, weighting equally each path label

mapping hypothesis



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The path label mapping ambiguity is solved using Averaged Likelihood Functions (ALFs) ALFs are built as mixtures of likelihood functions, weighting equally each path label

mapping hypothesis

ALFs are maximized over nodes coordinates and room maindimension

Maximum Averaged Likelihood (MAL) algorithm



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An extension of MAL algorithm is obtained including AoA and AoD“measurements” (i.e. still through cross-correlation) in the averaged likelihood extended MAL (eMAL)

Both algorithms share a tree approach

0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b



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0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e



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0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e

Low averaged likelihood





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0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e

Param d

Param f

Param e

Param a

Param c

Param b



56

2007





0 5 10 15 20 25 30 35-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e

Param d

Param f

Param e

Param a

Param c

Param bLow averaged likelihood





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…

…0 5 10 15 20 25 30 35

-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e

Param d

Param f

Param e

Param a

Param c

Param b

…



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…

…0 5 10 15 20 25 30 35

-1

-0.5

0

0.5

1

Time [ns]

Receiv

ed t

ensio

n [

mV

]


Param a

Param b

Param a

Param c

Param b

Param d

Param e

Param d

Param f

Param e

Param a

Param c

Param b

… Param b

Param a

Param c

Param d



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Ambiguities inherent to static scenarios

AoAs and AoDs are not a priori distinguishable: channel reciprocity

Tx/Rx ambiguity



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In the lack of global absolute references:

Tx/Rx ambiguity

4-image mirror ambiguity

true

configuration



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Tx/Rx ambiguity


true

configuration

symmetrical with

respect to Y axis

configuration



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Tx/Rx ambiguity


true

configuration

symmetrical with

respect to Y axis

configuration

symmetrical

with respect to

X axis

configurations



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Therefore, in a static scenario with only two peers, without absolute references, the joint anchor-less localization and environment characterization problem is affected by:

• path label mapping ambiguity (to be managed)

• Tx/Rx ambiguity (due to the scenario, not to be managed)

• 4-image mirror ambiguity (due to the absence of absolute references, not to be managed)

Tx/Rx ambiguity




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Results in the CR case

Tx/Rx and 4-mirror image ambiguities are compensated by a genius

• MAL and eMAL algorithms are just in charge of solving the path label mappingambiguity

1 1 1 1, , ,

????

Detection order Model order




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Estimation results obtained with a small, light and cheap antenna (i.e. Middle Alva):

MAL algorithm (i.e. AoA

and AoD information is

omitted)

extended MAL algorithm

(i.e. AoA and AoD

information is considered)

MAL positioning error CDF



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MAL algorithm (i.e. AoA

and AoD information is

omitted)

extended MAL algorithm

(i.e. AoA and AoD

information is considered)

eMAL positioning error CDF




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2007






eMAL positioning error CDF


Room dimension error CDF



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2007


CR case results: discussion

The previous results are best case results, as they assume a perfect compensation of Tx/Rx and 4-image mirror ambiguities

Tx/Rx ambiguity is expected to be (partially) solved by mobility



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CR case results: discussion

The previous results are best case results, as they assume a perfect compensation of Tx/Rx and 4-image mirror ambiguities

Tx/Rx ambiguity is expected to be (partially) solved by mobility

Coherent Receiver case assumes a high complexityreceiver

• very high frequency sampling and floating point precision are beyondthe State-of-the-Art of technology

Paths generally overlap, making the estimation of transmission parameters very challenging



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Introduction

Problem Statement

State-of-the-art




Summary



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2007


NCR case: principles (1/3)

Does the problem have a solution in the frame of the IEEE802.15.4a standard?

• bandwidth compliance, measurement refresh period, Pulse Repetition Period (PRP)



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Can the problem be solved targeting low power consumptionand low system complexity?

• State-of-the-Art sampling frequency is lower than 1 GHz

• for the sake of power consumption limitation, the Analog to Digital Converter (ADC) should have less than 6 bits



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Can the problem be solved targeting low power consumptionand low system complexity?

• State-of-the-Art sampling frequency is lower than 1 GHz

• for the sake of power consumption limitation, the Analog to Digital Converter (ADC) should have less than 6 bits

The Non-Coherent Receiver case assumes a low complexityreceiver, according to current State-of-the-Art

• under-Nyquist sampling frequency (i.e. 250 MHz)

• 4-bits precision of dynamic range



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The Energy Detection (ED) receiver satisfies the low complexity and low sampling frequency requirements

MHz250sf

bits4bitsN

dB10NF



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Because of the poor signal representation available, onlythe estimated paths ToAs are considered no need for a database of templates

Path overlapping still must be treated

True ToA



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Because of the poor signal representation available, onlythe estimated paths ToAs are considered no need for a database of templates

Path overlapping still must be treated

In order to solve the Tx/Rx remaining ambiguity, a dynamicscenario has to be considered

The Kalman Filter (KF) is a natural solver for a mobile estimation problem



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Tracking Algorithm (1/2)

ToAs of direct path and single-bounce paths considered as measurements making up the observation vector

Nodes coordinates and room dimensions are the hidden state variables to be estimated



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ToAs of direct path and single-bounce paths considered as measurements making up the observation vector

Nodes coordinates and room dimensions are the hidden state variables to be estimated

The hidden state variables are linked to measurements through non-linear (i.e. trigonometric) equations

The innovation is a function of the a priori predicted state and current observation :

with the innovation covariance matrix



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Path label mapping is treated considering the conditional decision variable

that is a Chi-Square variable with 5 degrees of freedom



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Path label mapping is treated considering the conditional decision variable

that is a Chi-Square variable with 5 degrees of freedom

Among the 4! = 24 possible path label mappings, the selected one at each time step is taken as the one showing the highest likelihood



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Tracking Algorithm Results (1/3)

Tracking assumption: assumed initial conditions close to real initial conditions unbiased ToAs estimation

X coordinate estimation for Tx X coordinate estimation for Rx

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Discrete time step

Tx n

orm

aliz

ed x

coord

inate

Tx normalized x coordinate, fs = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

True values

Mirror values

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Discrete time stepR

x n

orm

aliz

ed x

coord

inate

Rx normalized x coordinate, fs = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

True values

Mirror values

PDFs of estimated X coordinates as functions of the time step



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Y coordinate estimation for Tx Y coordinate estimation for Rx

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Discrete time stepR

x n

orm

aliz

ed y

coord

inate

Rx normalized y coordinate, fs = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

True values

Mirror values

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Discrete time step

Tx n

orm

aliz

ed y

coord

inate

Tx normalized y coordinate, fs = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

True values

Mirror values

PDFs of estimated Y coordinates as functions of the time step



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Y coordinate estimation for Tx Y coordinate estimation for Rx

Joint effect of

Tx/Rx and mirror ambiguities on coordinates



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Dx estimation RD estimation

10 20 30 40 50 60 70 80 90 1001

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Discrete time stepR

D v

alu

e

Room ratio RD, f

s = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

True values

10 20 30 40 50 60 70 80 90 1004

4.5

5

5.5

6

6.5

7

7.5

8

Discrete time step

Dx v

alu

e [

m]

Room main dimension Dx, f

s = 0.25 GHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

True values

PDFs of estimated room parameters as functions of the time step



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Dx estimation RD estimation

Environment variables are “locked”



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Introduction

Problem Statement

State-of-the-art




Summary



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2007


Conclusion

Conclusions :

• joint anchor-less localization and environment characterization is a challenging problem when only two SISO peers are considered

lots of ambiguities affect the estimation

once the ambiguities are removed, the estimates of the room main dimension and of the nodes positions may be reliable enough

• some techniques have been proposed to handle path label mappingambiguity

• the effect of 4-mirror image and Tx/Rx ambiguities on estimation has been investigated

1 1 1 1, , ,

????




88

2007


Conclusion

Conclusions :

• joint anchor-less localization and environment characterization is a challenging problem when only two SISO peers are considered

lots of ambiguities affect the estimation

once the ambiguities are removed, the estimates of the room main dimension and of the nodes positions may be reliable enough

• some techniques have been proposed to handle path label mappingambiguity

• the effect of 4-mirror image and Tx/Rx ambiguities on estimation has been investigated

• under more realistic assumptions (e.g. low complexity, IEEE 802.15.4a standard compliance) encouraging results as regards to nodes tracking (i.e. when assuming an initial state close enough to the true initial state and with unbiased ToAs estimates)



89

2007


Perspectives

Perspectives:

• studying the impact of using an improper database of templates

arbitrary elevation of the antennas

imperfect knowledge of the prior antenna and/or material characterization



90

2007


Perspectives

Perspectives:




• extending the algorithms to a sensor network with more than 2 nodes(i.e. measurement redundancy) and evaluating cooperative approaches for obtaining floor maps (i.e. sharing and merging of local information)

• investigating the NLOS case (i.e. filtering effects induced by transmission) and denser multipath profiles



91

2007


Perspectives

Perspectives:




• extending the algorithms to a sensor network with more than 2 nodes(i.e. measurement redundancy) and evaluating cooperative approaches for obtaining floor maps (i.e. sharing and merging of local information)

• investigating the NLOS case (i.e. filtering effects induced by transmission) and denser multipath profiles

• demonstrating the benefits of a raw knowledge of the indoor environment for the network optimization (e.g. context-aware routing protocols)

• compare estimation results obtained considering real measurements with them issued from simulations



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2007


Publications et Patents

Communications accepted to international conferences:

• V. La Tosa, B. Denis, B. Uguen, “Impact of Antennas on the Anchor-less Indoor Localization of a Static IR-UWB Pair”, IEEE PIMRC 2010, Istanbul, September 2010

• V. La Tosa, B. Denis, B. Uguen, “Maximum Averaged Likelihood Estimation Tree for Passive Localization Exploiting IR-UWB Multipath”, IEEE VTC-Spring’10, Taipei, May 2010

• V. La Tosa, B. Denis, B. Uguen, “A Preliminary Investigation on Angular Parameters Estimation in a Simplified IR-UWB Indoor Multipath Scenario”, IEEE WCNC’09, Budapest, April 2009

• V. La Tosa, B. Denis, F. Tchoffo-Talom, B. Uguen, “Joint Directions Finding and Material Typecasting through UWB Wireless Communications”, IEEE PIMRC’08, Cannes, September 2008

• V. La Tosa, B. Denis, B. Uguen, “Direct Path DoA and DoD Finding Through IR-UWB Communications”, IEEE ICUWB’08, Hanover, September 2008

PCT Patent:

• V. La Tosa, B. Denis, F. Tchoffo-Talom, B. Uguen, “Method and System for Aiding Environmental Characterization by Ultra Wide Band Radiofrequency Signals”, patent id PCT/EP2008/067569, November 2009

European project deliverables:

• V. La Tosa, et al., Deliverable 4.1.1 of European project ICT IP « eUWB », April 2009

• V. La Tosa, et al., Deliverable 4.1.2a of European project ICT IP « eUWB », May 2010



93

2007


2010

[email protected]

Thank you for your attention



94

2007


Direct path parameters estimation results

Going further…



95

2007


AoD-AoA joint error

Ep/N0 = 20 dB: CM antenna (a)

Cardioid antenna (b)



96

2007


Material recognition success rates

under square room assumption

Going further…



97

2007


Material Typecasting

Material recognition success rates:



98

2007


Parameters estimation

under rectangular room assumption

Going further…



99

2007


AoD-AoA joint error

PDFs of the AoIs in a rectangular room: largest edges (a)

smallest edges (b)



100

2007


Multipath Components

Cleaning Process

with Rejection Window

Going further…



101

2007


Iteration 1



102

2007


Iteration 2



103

2007


Iteration 3



104

2007


UWB Measurements

at Université de Rennes 1

(WHERE project)

Going further…



105

2007


True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF

UWB FP (5 Est. Path [TOA + Amp] wrt. 2 Rxs), EKFUWB FP (5 Est. Path [TOA + Amp] wrt. 4 Rxs), EKF

UWB Rx References (RSS/TOA/UWB FP)Base Station (FP Only)

0 10 20 30 40 50 60 70 80 90

0

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP(PDP), EKF

UWB FP (5 Est. Paths wrt. 2 Rxs), EKFUWB FP (5 Est. Path wrt. 4 Rxs), EKF

True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF

UWB FP (5 Est. Path [TOA + Amp] wrt. 2 Rxs), EKF


UWB Rx References (RSS/TOA/UWB FP)

Base Station (FP Only)

0 10 20 30 40 50 60 70 80 900

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF


UWB FP (5 Est. Paths wrt. 2 Rxs), EKF

UWB FP (5 Est. Path wrt. 4 Rxs), EKF

UWB measurements



106

2007


Real measurements of a mobile UWB channel

Acquisition time window

Tx p

ositio

n

(tra

jecto

ry)

True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF



0 10 20 30 40 50 60 70 80 90

0

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF



Energ

ie R

eçue (d

B)

UWB measurements



107

2007


Deterministic simulations, i.e. Ray-Tracing toolReal measurements of a mobile UWB channel


Tx p

ositio

n

(tra

jecto

ry)

True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF



0 10 20 30 40 50 60 70 80 90

0

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF



True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF





0 10 20 30 40 50 60 70 80 900

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF




Energ

ie R

eçue (d

B)

UWB measurements



108

2007


True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF



0 10 20 30 40 50 60 70 80 90

0

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF



UWB measurements

Deterministic simulations, i.e. Ray-Tracing toolReal measurements of a mobile UWB channel


Tx p

ositio

n

(tra

jecto

ry)

True track

RSS+TOA, EKF

FP (PDP), EKF

RSS+TOA+FP, EKF





0 10 20 30 40 50 60 70 80 900

2

4

Time [s]

Positio

n e

rror

[m]

RSS+TOA, EKF

FP (PDP), EKF




Energ

ie R

eçue (d

B)

Multipath signal can be reliably predicted



109

2007


Simulation parameters

for MAL and eMAL

algorithms

Going further…



110

2007


MAL and eMAL parameters

technical presentation vlt 10 12 03

Documents