fila: fine-grained indoor localization -...
TRANSCRIPT
FILA:
Fine-grained Indoor Localization
Kaishun Wu, Jiang Xiao, Youwen Yi, Min Gao, Lionel M. Ni
IEEE 2012 INFOCOM
March 29th, 2012
Hong Kong University of Science and Technology
Outline
Introduction
Motivation
System Design
Performance Evaluation
Conclusions
2
3
Assert Tracking
Emergence Response Healthcare
Security
Social Network
Goals:
To fast locate objects
To obtain high accuracy
To minimize deployment costs
Indoor Location-based Services
3
Techniques System Accuracy Cost
GPS SnapTrack >5m Medium
GSM GSM Fingerprinting >2.5m Medium
Ultrasonic Criktet 4*4 Sq Ft (100%) High
Infrared Active Badge 5-10m Medium
UWB Ubisense 15cm High
RFID LANDMARC 2m Low
Sensors RIPS 3cm High
WLAN RADAR, Horus 3-5m; 2m Low
RF-based
Indoor Localization Techniques
Radio Frequency (RF) is the frequency that the radio signals
are carried and transmitted from the antenna.
Existing RF-based Indoor Localization Techniques
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Highest!
Lowest!
WLAN Cost
Accuracy
WLAN Advantages
Dense-deployed APs
Prevalent WiFi-enabled devices
Low cost and easy for implementation
5
WLAN RSSI-based
Indoor Localization Techniques
Received Signal Strength Indicator (RSSI)
a measurement of the power present in a received radio signal
Radio propagation model: distance=f(RSSI)
Free space path loss model
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Distance
RSS
I
Free Space Path loss
RSSI AP
WiFi-enabled device
Related Work
RADAR [INFOCOM’00] , Horus [Mobisys’05]
Fingerprint: signal strength radio map
Accuracy: 3m for 50%, 0.7m for 50%
Wideband Powerline Positioning [UbiComp’08]
Apply wideband frequency to mitigate the time variance.
Accuracy: reduce accuracy degradation over time
Indoor localization without pain [MobiCom’10]
Radio propagation model based
Accuracy: 2m for 50%
7
All based on
RSSI
Is
RSSI
a reliable indicator?
8
Observation
RSSI value is a packet-level estimator
Average the signal power over a packet.
RSSI is easily varied by multipath.
9
Constructive Destructive
Multipath Path loss
RSSI is not reliable !
Could we find
a reliable metric to improve indoor localization?
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Key Insight
Orthogonal Frequency Division Multiplexing
IEEE 802. 11 a/g/n leverage OFDM to provide high throughput
In OFDM, a channel is orthogonally divided into multiple sub
channels, namely subcarriers
Data is transmitted in parallel on multiple subcarriers that
overlap in frequency
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1st Subcarrier 2nd Subcarrier 3rd Subcarrier
IFFT Data in
Modulation D/A
Baseband O
FDM signal Transmitter
FFT Data out
Modulation A/D
Baseband O
FDM signal Receiver
Key Insight
Channel State Information
In OFDM system, the received signal over multiple subcarriers is
Y = H X + N (X– transmit signal, N– noise)
H=Y/X -- Channel State Information (CSI)
H=hejw(h: amplitude, w: phrase)
CSI is the channel response at the receiver in frequency domain
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Data out
Channel
Data in Encoder
Transmitter Receiver
Decoder X Y
+ x
N H
Key Insight
.
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RSSI
RSSI estimates the channel
in packet level.
Only a single amplitude
CSI
CSI estimates the channel in
subcarrier level. [1]
Vector with amplitude and
phase
RSSI
CSIs
Packet 2.4GHz
antenna
Receiver S/P FFT
Baseband
[1]D Halperin and et al., “Predicable 802.11 Packet Delivery from Wireless Channel Measurements”, in SIGCOMM, 2010.
So compared to RSSI,
CSI Is
Fine Grained metric full of
frequency domain information!
We expect to exploit such
information to obtain a reliable
indicator for location.
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Scope
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Motivation • RSSI is inaccurate and not reliable
• CSI is fine grain information
Approach • Replace RSSI with CSI
• Design a FILA system
Goal • Improve the indoor
localization performance
Outline
Introduction
Motivation
System Design
Performance Evaluation
Conclusions
16
Cross Layer Architecture
17
Tx AP Location
Information
(2) Process CSI CSIeff (2)’ Distance
Calculator
OFDM
Demodulator
OFDM
Decoder Rx
Normal
Data
+ (3) Locate Rx
Network
layer
Pysical layer
Cross layer
(1) Collect CSI
Channel
Estimation
AP1
d2 AP2
AP3
d1
Design Approach
CSI Collection
Process CSI and Distance Estimation
Location Determination
18
Approach (1st Step)
The first step is to collect the subcarriers CSI which divided into 30
groups on the received baseband in WLAN.
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CSI Collection Process CSI Location
Hardware Wireless card
Operating SystemDevice Driver
Location Determination System
Wireless API
Approach (2nd Step)
Two processing mechanisms:
#1 Time-domain Multipath Mitigation
#2 Frequency-domain Fading Compensation
Distance Estimation
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CSI Collection Process CSI Location
Approach (2nd Step)
Time-domain Multipath Mitigation
The received signal is the combination of
multiple reflections with LOS signal
If bandwidth is wider than coherence
bandwidth, the reflections will be
resolvable.
The bandwidth of 802.11n is 20MHz, that
provides the capability of the receiver to
resolve the different reflections in the
channel.
h=IFFT(CSI) 21
CSI Collection Process CSI Location
0 20 40 600
5
10
15
20
25
Time delay
Ch
ann
el R
esp
on
se A
mp
litu
de
Approach (2nd Step)
Frequency-domain Fading Compensation
When the space between two subscarriers is larger than coherence
bandwidth, they are fading independently
22
CSI Collection Process CSI Location
Exploit the frequency diversity of CSI to
eliminate small-scale fading
We define effective CSI as the weighting
average among all subcarriers
-30 -20 -10 0 10 20 30-28
-27
-26
-25
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-23
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Subcarrier Index
Re
ce
ive
Po
we
r(d
Bm
)
CSI𝑒𝑓𝑓 =1
𝐾
𝑓𝑘
𝑓0
𝐾𝑘=1 × 𝐶𝑆𝐼𝑘 , k ∈ [−15,15]
Approach (2nd Step)
Distance Determination
Refined model: distance= f(CSIeff )
d =1
4𝜋
𝑐
𝑓0× 𝐶𝑆𝐼𝑒𝑓𝑓
2
× 𝛿
1
𝑛
δ: environment factor
𝓃: path loss fading exponent
KNN algorithm
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CSI Collection Process CSI Location
Initialized δ and 𝓃
Choose a CSIeff dataset corresponding to a distance,
and then train δ and 𝓃
Use δ,𝓃 to verify the CSIeff dataset of other distances
Other distances are in conformity with the training δ
and 𝓃
Approach (3rd Step)
Obtain the coordinates of the APs.
Calculate the distance between object and the APs.
Apply the trilateration method to locate object.
𝑑1 = 𝑥1 − 𝑥02 + 𝑦1 − 𝑦0
2
𝑑2 = 𝑥2 − 𝑥02 + 𝑦2 − 𝑦0
2
𝑑3 = 𝑥3 − 𝑥02 + 𝑦3 − 𝑦0
2
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CSI Collection Process CSI Location
AP1
AP2 AP3
So, we can determine the location of the !
Outline
Introduction
Motivation
System Design
Performance Evaluation
Conclusions
25
Experimental Setup
Router iwl5300
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Hardware
Intel WiFi Link 5300, 802.11n router
Software
Linux 2.6.38 kernel, Matlab, Python
Implementation (4 Scenarios)
Chamber
5m Χ 8m
Lab Lecture Hall
20m Χ 25m 3m Χ 4m
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Corridor
Evaluation Metric
Temporal stability
Accuracy
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Relation between CSI and Distance
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2.5 3 3.5 4 4.5 5 5.5 610
15
20
25
30
35
40
45
50
Distance (meters)
CS
I eff a
mp
litu
de
CSIeff amplitude
Exponential Fitting
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Temporal Stability
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Accuracy of Distance Estimation
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Location Accuracy in Lab
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For over 90% of data points, the localization error < 1m
For over 50% of data points, the localization error < 0.5m
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Location Accuracy in Lecture Hall
For over 90% of data points, the localization error < 1.8m
For over 50% of data points, the localization error < 1.2m
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Location Accuracy in Corridor
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For over 90% of data points, the localization error < 2m
For over 50% of data points, the localization error < 1.2m
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Outline
Introduction
Motivation
System Design
Performance Evaluation
Conclusions
35
Conclusions
36 We use fine gained PHY information (CSI) in OFDM-based
WLANs to improve indoor localization performance.
We design FILA, a fine grained cross layer localization system
leveraging CSI based on existing WLAN standards.
Experiments with commercial NICs in different scenarios
show that FILA can achieve significantly accuracy gain
comparing with corresponding RSSI methods.
36