1. 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. 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. 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. 4 Localizing and Tracking Targets in Distributed Sensor Network

5. 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. 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. 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. 8 Sequential Bayesian Estimation – : System transition function – : Measurement function where: – : state vector – : Observation vector – : System noise vector, white and independent of past and current states with known PDF – : measurement noise vector, white and independent of past, present states and system noise with known PDF

9. 9 Tracking with Particle Filtering Represents the required PDF as a set of random samples, Two Steps – Predict Step – Update Step – Resample – Update States ;

10. 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

11. 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 – g i : gain factor of i th sensor – s k (t): energy emitted by the k th source –  k (t) Source k’s location – r i : Location of the i th sensor –  i (t): sum of background additive noise and the parameter modeling error. – K: the number of the sources

12. 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:

13. 13 Cooperating ML estimator with Particle Filtering Measurement Likelihood for given estimated target locations: – where : a function of : Projection matrix Unknown Parameters Need at least K(p+1) sensors, p is the dimension of the location Nonlinear Problem Therefore: ;

14. 14 Particle Filter in Distributed Framework

15. 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. 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. 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. 18 Localization Results (Comparison of ML and Particle Filtering )

19. 19 Simulation Results for Multiple Targets Tracking Tracking two targets moving in opposite direction Bigger random noise are added at random time

20. 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

21. 21 The End http://www.ece.wisc.edu/~sensit/ Thanks

22. 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

23. 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. 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

25. 25 Distributed Particle Filter Parallel Run Particle Filtering at each Layer 2 Manager Node M=4, L=2

26. 26 Distributed Particle Filtering ith Layer2 manager node: – Calculate the number of particles at its sub-region with refined grids, total M 2 N ik, k=1,2,…M 2 – Calculate the number of particles at the other sub-region, P j, j=1,2,…L 2, j  i, Manager Node decode: – For location belongs to sub-region I Each grid k – Target Location,

27. 27 Distributed Particle Filtering Encoding Particles Maximum Bits Required for Transmission Resolution: – where: L 2: the number of layer 2 M 2: 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