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

1. 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 sheng@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

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 Node Detection Node Classi- 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

8. 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

9. 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 kthsource • k(t) Source k’s location • ri: Location of the ithsensor • i(t): sum of background additive noise and the parameter modeling error. • K: the number of the sources

10. Let be the Euclidean distance between sensor i and target j, and Also define and Then, the energy attenuation model can be represented as: Measurement Model-Notation

11. : a function of : Projection matrix Cooperating ML estimator with Particle Filtering • Measurement Likelihood for given estimated target locations: • where Unknown Parameters ; Therefore: Need at least K(p+1) sensors, p is the dimension of the location Nonlinear Problem

12. Particle Filter in Distributed Framework

13. 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

14. 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

15. 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

16. Localization Results(Comparison of ML and Particle Filtering )

17. Simulation Results for Multiple Targets Tracking • Tracking two targets moving in opposite direction • Bigger random noise are added at random time

18. 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

19. The End http://www.ece.wisc.edu/~sensit/ Thanks

20. 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

21. 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

22. 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

23. Distributed Particle Filter • Parallel Run Particle Filtering at each Layer 2 Manager Node M=4, L=2

24. 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 • Target Location,

25. 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