In this paper, we present a distributed particle filter (DPF) for target tracking in a sensor network. The proposed DPF consists of two major steps. First, particle compression based on support vector machine is performed to reduce the cost of transmission among sensors. Second, each sensor fuses the compressed information from its neighboring nodes with use of consensus or gossip algorithm to estimate the target track. Computer simulations are included to verify the effectiveness of the proposed approach.
1. Patwari, N., J. N. Ash, S. Kyperountas, A. O. Hero III, R. L. Moses, and N. S. Correal, "Locating the nodes --- Cooperative localization in wireless sensor networks," IEEE Signal Processing Magazine, Vol. 22, No. 4, 54-69, Jul. 2005. doi:10.1109/MSP.2005.1458287
2. Guo, D. and X. Wang, "Dynamic sensor collaboration via sequential Monte Carlo," IEEE Journal on Selected Areas in Communications, Vol. 22, No. 6, 1037-1047, Aug. 2004. doi:10.1109/JSAC.2004.830897
3. Denantes, P., F. Benezit, P. Thiran, and M. Vetterli, "Which distributed averaging algorithm should I choose for my sensor network?," Proc. 27th IEEE Conf. Computer Communications and Networks, 986-994, St. Thomas, U.S. Virgin Islands, Apr. 2008.
4. Olfati-Saber, R., "Distributed Kalman filter with embedded consensus filters," Proc. 44th IEEE Conf. Decision and Control and the European Control Conference, 8179-8184, Seville, Spain Dec. 2005.
5. Coates, M. J., "Distributed particle filtering for sensor networks," Proc. Int. Symp. Information Processing in Sensor Networks, 99-107, Berkeley, CA, Apr. 2004.
6. Ristic, B., A. Arulampalam, and N. Gordon, Beyond the Kalman Filter-particle Filters for Tracking Applications, Artech House, Boston, 2004.
7. Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. on Signal Processing, Vol. 50, No. 2, 174-188, Feb. 2002. doi:10.1109/78.978374
8. Sheng, Y., X. Hu, and P. Ramanathan, "Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor networks," Proc. 4th Int. Symposium on Information Processing in Sensor Networks, 181-188, Los Angeles, California, USA, Apr. 2005.
9. Gu, D., "Distributed particle filter for target tracking," Proc. IEEE International Conference on Robotics and Automation, 3856-3861, Roma, Italy, Apr. 2007.
10. Weston, J., A. Gammerman, M. Stitson, V. Vapnik, V. Vovk, and C. Watkins, "Support vector method for multivariate density estimation," Advances in Kernel Methods: Support Vector Machines, 293-306, MIT Press, Cambridge, MA, 1998.
11. Vapnik, V. N. and S. Mukherjee, "Support vector method for multivariate density estimation," Tech. Rep., No. 1653, A.I. Memo, MIT AI Lab., 1999.
12. Burges, C. J. C., "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, Vol. 2, 121-167, Jun. 1998. doi:10.1023/A:1009715923555
14. Boyd, S., A. Ghosh, B. Prabhakar, and D. Shah, "Randomized gossip algorithms," IEEE Trans. on Information Theory, Vol. 52, No. 6, 2508-2530, Jun. 2006. doi:10.1109/TIT.2006.874516
15. Aysal, T. C., M. E. Yildiz, and A. Scaglione, "Broadcast gossip algorithms," Proc. IEEE Information Theory Workshop, 343-347, Porto, Portugal, May 2008.
16. Liu, H. Q., H. C. So, K. W. K. Lui, and F. K. W. Chan, "Sensor selection for target tracking in sensor networks," Progress In Electromagnetics Research, PIER 95, 267-282, 2009.
17. Liu, H. Q. and H. C. So, "Target tracking with line-of-sight identification in sensor networks under unknown measurement noises," Progress In Electromagnetics Research, PIER 97, 373-389, 2009.
18. Chen, J. F., Z. G. Shi, S. H. Hong, and K. S. Chen, "Grey prediction based particle filter for maneuvering target tracking," Progress In Electromagnetics Research, PIER 93, 237-254, 2009.
19. Khodier, M. M. and M. Al-Aqeel, "Linear and circular array optimization: A study using particle swarm intelligence," Progress In Electromagnetics Research B, Vol. 15, 347-373, 2009. doi:10.2528/PIERB09033101
20. Vapnik, V. N., Statistical Learning Theory, John Wiley, 1998.
21. Olfati-Saber, R. and J. S. Shamma, "Consensus filters for sensor networks and distributed sensor fusion," Proc. 44th IEEE Conference on Decision and Control, and the European Control Conference, 6698-6703, Seville, Spain, Dec. 2005.