Transcript Maximum Likelihood Energy Based Acoustic Source Localization

Sequential Acoustic Energy Based
Source Localization Using Particle
Filter in a Distributed Sensor Network
Xiaohong Sheng, Yu-Hen Hu
University of Wisconsin – Madison
Dept. Electrical and Computer Engineering
[email protected]
http://www.ece.wisc.edu/~sensit/
1
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
2
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
3
Localizing and Tracking Targets in
Distributed Sensor Network
1200 m
Northern
Checkpoint
~1300 m
Defile
Sandia
Autonomous
Mobile Robot
OpArea
1000 m
800 m
100baseT Hardwire
Experiment Control
Ethernet
600 m
Gateway/Imager
400 m
RF
Ethernet
200 m
25 Nodes @
Intersection
Eastern Checkpoint
~500 m//intersection
Intersection
Western Checkpoint
~ 400 m//intersection
Base Camp
~300 m//intersection
4
UWCSP: Univ. Wisconsin Collaborative
Signal Processing
Node
Detection
Node
Classification
•
Received Det./Classify report from nodes
•
N
Fault Tolerance Check
– Energy Detection
– Node target classification
Y
N
Region Detection Fusion
Y
Classification
fusion
Target type
Current track DB
Energy-based
localization
Target location
Track associate
N
Current target?
Create new track
Distributed Signal
Processing Paradigm
(Local) Node signal
processing
•
(Global) Region signal
processing
– Region detection and
classification fusion
– Energy based
localization
– particle filter tracking
– Hand-off policy
Y
Update track and predict
Send info to next region
Y
Handoff
N
5
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
6
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
7
System Model for tracking vehicle in
sensor field
• System Model:
a k (t )  w(t )
u k (t )  u k (t  1)  a k (t )T
1
ρ k (t )  ρ k (t  1)  u t (t  1)T  a k (t )T 2
2
• State Vector for source k at time t is:
k
αt
 ρk (t ) u k (t ) a k (t )
where:
a t (t ) : Acceleration of the source k at time t
ρ t (t ) : Velocity of the source k at time t
u t (t ) : Location the source k at time t
T:
Time Interval between two consecutive computation
10
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
K
sk t 
yi t   ys t    i t   gi 
  i t 
2
k 1 ρ t   r
k
i
–
–
–
–
–
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
11
Measurement Model-Notation
• Let d ij t   ρ j t   ri
be the Euclidean distance
xt
between sensor i and target j,
and
 1
 d 2 (t )
 11
 1
D t   d 2 (t )
 21
 1

 d N2 1 (t )
• Also define
 y t   1 t 
 1
  1 (t )
y2 t    2 (t )

 2 (t )
y N (t )   N (t ) 

 N (t )

st  s1 (t ) s2 (t )  sK (t )
1
1 

 g1
gN 
g2

2
2
d12 (t )
d1K (t )  G t  diag
,
, ,

 N (t ) 
 1 (t )  2 (t )
1
1 

2
and
d 22 (t )
d 22K (t ) 
Ht  G t Dt



 
1
1
 • Then, the energy attenuation

2
model can be represented as:
d N2 2 (t )
d NK
(t ) 
xt  G t Dt st  ξ t  Ht st  ξ t
~ N Ht st , I 
12
T
Cooperating ML estimator with Particle
Filtering
• Measurement Likelihood for given estimated target locations:
ln P(z t | θt )  z t  H t s t Γ z t  H t s t   xTt Pt x t
– where
θt  ρ1 (t )  ρ K (t ) s1 (t )  s K (t )
st  Ht xt
;


Pt 
H t : a function of 1:K (t )
1 T
H
Ht Ht Ht Ht
Therefore:
Unknown Parameters
p

: Projection matrix
*
x k |  k (i )
  e
xTk Pk ( k* ( i )) x k
Need at least K(p+1) sensors, p is the dimension of the location
Nonlinear Problem
13
Particle Filter in Distributed Framework
Layer 2 sub-region
Layer 1 sensor Region
Layer 1 Manager node
Layer 2 Manager Node
Layer 2 Detection Node
14
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
15
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
16
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
17
Localization Results
(Comparison of ML and Particle Filtering )
18
Simulation Results for Multiple Targets
Tracking
• Tracking two targets moving in opposite direction
• Bigger random noise are added at random time
19
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
20
The End
http://www.ece.wisc.edu/~sensit/
Thanks
21
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
1200 m
Northern
Checkpoint
~1300 m
Defile
Sandia
Autonomous
Mobile Robot
OpArea
1000 m
– acoustic, seismic and Polorized infrared
(PIR) sensors,
– 16-bit micro-prcessor,
– radio transceiver and modem.
800 m
Sensor node is powered by external car
battery
Military vehicles were driven through the
road.
200 m
100baseT Hardwire
Experiment Control
Ethernet
600 m
Gateway/Imager
400 m
RF
Ethernet
25 Nodes @
Intersection
Eastern Checkpoint
~500 m//intersection
Intersection
Western Checkpoint
~ 400 m//intersection
Base Camp
~300 m//intersection
– AAV ( Amphibious Assault Vehicle),
– DW ( dragon wagon)
•
Sampling rate : 4960 Hz at 16-bit
resolution
22
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
23
Distributed Particle Filter-Node Function
• Layer 2 Detection Node
– BroadCast with Lower Transmission Power
– BroadCast with Delayed Time
dt 
1
SNR
• 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
24
Distributed Particle Filter
• Parallel Run Particle Filtering at each Layer 2 Manager Node

M
Layer 2 sub-region


Layer 1 sensor Region
L
M=4, L=2
25
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, ji,
• Manager Node decode:
– For location belongs to sub-region I
• Each grid k
N i'
N ik 
nik 
N
M2
L2
k 1
j 1, j i
 N ik   Pj
N
nik
– Target Location,
 rˆ    y i  1  k  1r  r 2


rˆx     i  1  k  1rx  rx 2 y
y
y
L
i

1
k

1


i 1 k 1 L

L2 M 2  R
x
L2 M 2  R
26
Distributed Particle Filtering
• Encoding Particles
Layer 2 ID
Number of particles occurs in Location ID at
the other Layer 2 sub-region Layer 2 Region
( L  1) log 2 N / L2
log 2 L2
log 2 M
2
Number Occurs at the
corresponding Location
log 2 N / L2
• Maximum Bits Required for Transmission
( L2  1  M 2 )(log
• Resolution:
r
2
L2  log 2 M 2  log 2
N
2
L
)
Rs
M 2 L2
– 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
27