Xiaohong Sheng, Yu-Hen Hu University of Wisconsin – Madison

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Sequential Acoustic Energy Based Source Localization Using Particle

Filter in a Distributed Sensor Network

Xiaohong Sheng, Yu-Hen HuUniversity of Wisconsin – Madison

Dept. Electrical and Computer [email protected]

http://www.ece.wisc.edu/~sensit/

mailto:[email protected]

2

Outline

• Wireless Sensor Network– New features of recent sensor devices– Applications– Acoustic Source Localization and Tracking Problems

• Available algorithms• Our approach

• Source Localization using particle filtering in sensor network – Particle filtering framework– System model– Measurement model

• Energy decay model• Cooperate ML Algorithm with particle filtering

• Apply particle filter into a distributed framework• Experiments and Simulation• Conclusion

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Sensor Network• New sensor nodes

– Integrating micro-sensing and actuation

– On-board processing and wireless communication capabilities

– Limited communication bandwidth

– Limited power supply

• Provides a novel signal processing platform– Detection, classification– Localization, tracking etc

Sitex 02 experiment sensir field

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Localizing and Tracking Targets in Distributed Sensor Network

NorthernCheckpoint

~1300 m

Base Camp~300 m//intersection

200 m

400 m

600 m

1000 m

800 m

1200 m

Intersection

Gateway/Imager

RF Ethernet

DefileSandia

Autonomous Mobile Robot

OpArea

100baseT HardwireExperiment Control

Ethernet

Eastern Checkpoint~500 m//intersection

Western Checkpoint~ 400 m//intersection

25 Nodes @Intersection

NorthernCheckpoint

~1300 m


200 m

400 m

600 m

1000 m

800 m

1200 m

Intersection

Gateway/Imager

RF Ethernet

DefileSandia


OpArea


Ethernet




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UWCSP: Univ. Wisconsin Collaborative Signal Processing

• Distributed Signal Processing Paradigm

• (Local) Node signal processing– Energy Detection– Node target classification

• (Global) Region signal processing– Region detection and

classification fusion– Energy based

localization– particle filter tracking– Hand-off policy

Region Detection Fusion

Energy-basedlocalization

Classificationfusion

Target locationTarget type

Track associate

Current target?

Update track and predict

HandoffSend info to next region

Create new track

Current track DB

Y N

Y

N

N

Y

Received Det./Classify report from nodes

Fault Tolerance Check

Y

N

NodeDetection

NodeClassi-fication

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Source Localization and Tracking in wireless Sensor Network

• Available Localization and Tracking method– Localization Estimation Modeling

• CPA, Beamforming, TDOA

– Tracking Method• Sequential Bayesian Estimation

– Kalman Filtering, Extended Kalman Filtering– Grid-Based Bayesian Estimation –Exhaustive Search

• Our Approach– Previously

• Intensity Based Source Localization

• ML estimation and Non-Linear estimation

– This Paper• Particle Filtering cooperated with ML estimation

• Distributed Framework

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Outline





– Apply particle filter into a distributed framework

• Experiments and Simulation• Conclusion

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System Model for tracking vehicle in sensor field

• System Model:

• State Vector for source k at time t is:

where:

: Acceleration of the source k at time t

: Velocity of the source k at time t

: Location the source k at time t

T: Time Interval between two consecutive computation

)()( ttk wa Tttt kkk )()1()( auu

2)(2

1)1()1()( TtTttt ktkk auρρ

)(tta

)(ttu)(ttρ

)()()( ttt kkkkt auρα

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Measurement Model-Acoustic Delay Function

• Source Energy attenuates at a rate that is inversely proportional to the Square of the distance to the source

• Energy Received by each Sensor is the Sum of the Decayed Source Energy

– gi: gain factor of ith sensor

– sk(t): energy emitted by the kth source

– k(t) Source k’s location

– ri: Location of the ith sensor

– i(t): sum of background additive noise and the parameter modeling error.

– K: the number of the sources

tt

tsgttyty i

K

kik

kiisi

12

rρ

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Measurement Model-Notation

• Let

be the Euclidean distance between sensor i and target j, and

• Also define

and

• Then, the energy attenuation model can be represented as:

ijij ttd rρ

)(

1

)(

1

)(

1

)(

1

)(

1

)(

1)(

1

)(

1

)(

1

222

21

22

222

221

21

212

211

tdtdtd

tdtdtd

tdtdtd

NKNN

K

K

t

D

T

N

NNt t

tty

t

tty

t

tty

)(

)()(

)(

)(

)( 2

22

1

11

x

)()()( 21 tststs Kt s

)(

,,)(

,)( 2

2

1

1

t

g

t

g

t

gdiag

N

Nt

G

ttt DGH

IsH

ξsHξsDGx

,~ tt

tttttttt

N

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Cooperating ML estimator with Particle Filtering

• Measurement Likelihood for given estimated target locations:

– where

ttTtttttttttP xPxsHzsHzθz Γ )|(ln

)()()()( 11 tststt KKt ρρθ

tH : a function of )(:1 tKttt xHs

Ttt

Httt HHHHP

1 : Projection matrix

Unknown Parameters

Need at least K(p+1) sensors, p is the dimension of the location

Nonlinear Problem

kkkTk iP

kk eip xxx ))((* *

)(| Therefore:

;

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Particle Filter in Distributed FrameworkLayer 2 sub-region Layer 1 sensor Region

Layer 1 Manager node

Layer 2 Manager Node

Layer 2 Detection Node

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Distributed Particle Filter-Node Function

• Layer 2 Detection Node – BroadCast with Lower Transmission Power

• Layer 2 Manager Node – Encode the data received from its layer 2 detection node– BroadCast with higher Transmission Power– Distributed Particle Filter– Encode Particles– Send to Manager Node

• Layer 1 Manager Node– Pear to Pear Transmission with the highest Transmission

Power, – But only when it predicts the targets will move to its neighboring

sensor region

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Outline





– Apply particle filter into a distributed framework

• Experiments and Simulation• Conclusion

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Application to Field Experiment Data

• Sensor Field is divided into two sensor region, i.e.,

Region 1 and Region 2• For region 1, Node 1 is

manager node, others are detection nodes

• For region 2, Node 58 is manager node, others are detection nodes

Sensor deployment, road coordinate and region specification for experiments

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Localization Results(Comparison of ML and Particle Filtering )

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Simulation Results for Multiple Targets Tracking

• Tracking two targets moving in opposite direction• Bigger random noise are added at random time

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Future Work

– Conclusion• Develop an energy-efficient, band-width efficient, practically

applicable, accurate and robust source localization method.

• The algorithm can be incorporated in a wireless sensor network to detect and locate multiple sound sources effectively.

• The algorithm is activated on demands

• The algorithm can be fit into the distributed sensor network framework.

– Future Work• Integration EBL with sub-array beam-forming

• Distributed Propagating Parameters In Stead of Encoded Particles

• Find a better way of brief and state propagating

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The End


Thanks


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Experiments

• Experiment was carried out in Nov. 2001, Sponsored by DARPA ITO SensIT project at 29 Palms California, USA

• Sensor nodes are laid out along side a road

• Each sensor node is equipped with – acoustic, seismic and Polorized infrared

(PIR) sensors, – 16-bit micro-prcessor, – radio transceiver and modem.

• Sensor node is powered by external car battery

• Military vehicles were driven through the road. – AAV ( Amphibious Assault Vehicle),– DW ( dragon wagon)

• Sampling rate : 4960 Hz at 16-bit resolution

NorthernCheckpoint

~1300 m


200 m

400 m

600 m

1000 m

800 m

1200 m

Intersection

Gateway/Imager

RF Ethernet

DefileSandia


OpArea


Ethernet




NorthernCheckpoint

~1300 m


200 m

400 m

600 m

1000 m

800 m

1200 m

Intersection

Gateway/Imager

RF Ethernet

DefileSandia


OpArea


Ethernet




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Significance

• Our localization and tracking algorithm will partially address the limitations of the existing algorithms:– Robust to unknown and unexpected disturbance

• Background noise,

• Interference signals

• Wind gust,

• Faulty and drifting sensor readings

• Failures of sensor nodes and wireless communication network

– Less Strict Requirement of Synchronization – Feasible to localize multiple targets

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Distributed Particle Filter-Node Function• Layer 2 Detection Node

– BroadCast with Lower Transmission Power– BroadCast with Delayed Time

• Layer 2 Manager Node – Forward received data with higher transmission power– Distributed Particle Filtering– Encode Particles– Send encoded particles to Manager Node

• Layer 1 Manager Node– Pear to Pear Transmission with the highest Transmission Power, – But only when it predicts the targets will move to its neighboring sensor

region

SNRd t

1

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Distributed Particle Filter• Parallel Run Particle Filtering at each Layer 2 Manager Node

M=4, L=2

Layer 2 sub-region Layer 1 sensor Region

M L

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Distributed Particle Filtering

• ith Layer2 manager node:– Calculate the number of particles at its sub-region with refined

grids, total M2

• Nik, k=1,2,…M2

– Calculate the number of particles at the other sub-region, • Pj, j=1,2,…L2, ji,

• Manager Node decode:– For location belongs to sub-region I

• Each grid k

– Target Location,

ik

L

ijjj

M

kik

iki

ik nN

PN

nN

NN

22

,11'

2 2

1 1211ˆ

L

i

M

kxx

xx rrki

L

Rr

2 2

1 1211ˆ

L

i

M

kyy

yy rrki

L

Rr

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Distributed Particle Filtering• Encoding Particles

• Maximum Bits Required for Transmission

• Resolution:

– where: • L2: the number of layer 2• M2: the number of grids at layer 2• N: the number of total particles used for particle filtering• Rs: Region Size

– For N=512, M=4,L=2, Rs=64, R<247 Bits/T, r=8– For N=512, M=2, L=2, Rs=64, R<77 Bits/T, r=16

Layer 2 IDLocation ID at

Layer 2 RegionNumber Occurs at the

corresponding Location

22log L

22log M 2

2 /log LN

Number of particles occurs inthe other Layer 2 sub-region

22 /log)1( LNL

22LM

Rsr

)loglog)(log1(22

22

22

22

L

NMLML

Xiaohong Sheng, Yu-Hen Hu University of Wisconsin – Madison

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