1 sequential acoustic energy based source localization using particle filter in a distributed sensor...
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1
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 Engineeringsheng@cae.wisc.edu
http://www.ece.wisc.edu/~sensit/
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
3
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
4
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
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
5
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
6
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
7
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
10
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
11
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
12
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
13
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:
;
14
Particle Filter in Distributed FrameworkLayer 2 sub-region Layer 1 sensor Region
Layer 1 Manager node
Layer 2 Manager Node
Layer 2 Detection Node
15
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
16
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
17
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
18
Localization Results(Comparison of ML and Particle Filtering )
19
Simulation Results for Multiple Targets Tracking
• Tracking two targets moving in opposite direction• Bigger random noise are added at random time
20
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
22
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
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
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
23
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
24
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
25
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
26
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
27
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
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