Vol. 11
Latest Volume
All Volumes
PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2009-12-04
Distributed Particle Filter for Target Tracking in Sensor Networks
By
Progress In Electromagnetics Research C, Vol. 11, 171-182, 2009
Abstract
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.
Citation
Hong-Qing Liu Hing-Cheung So Frankie Kit Wing Chan Kenneth Wing Kin Lui , "Distributed Particle Filter for Target Tracking in Sensor Networks," Progress In Electromagnetics Research C, Vol. 11, 171-182, 2009.
doi:10.2528/PIERC09092905
http://www.jpier.org/PIERC/pier.php?paper=09092905
References

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

13. Smola, A. J. and B. Scholkopf, "A tutorial on support vectorv regression," Statistics and Computing, Vol. 14, No. 3, 199-222, Aug. 2004.
doi:10.1023/B:STCO.0000035301.49549.88

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.