CN  Vol.2 No.1 , February 2010
ADPF Algorithm for Target Tracking in WSN
Abstract: Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes’ computing capacity. In this paper, a novel filtering method—asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorithm. Firstly, instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred which can reduce the bandwidth and power consumption. Secondly, in order to use sampling information effectively, when target moving to the next cluster head region, the GMM parameters are transfer to the next cluster head, and combine with the new local GMM parameters to compose the new GMM parameters incrementally. The ADPF can also deal with the situation of different number of nodes in different cluster when using the dynamic cluster structure. The proposed ADPF is compared to some other DPF for WSN target tracking, and the experimental results show that not only the precision is improved, but also the bandwidth and power is reduced.
Cite this paper: nullC. Song, H. Zhao, W. Jing and D. Liu, "ADPF Algorithm for Target Tracking in WSN," Communications and Network, Vol. 2 No. 1, 2010, pp. 50-53. doi: 10.4236/cn.2010.21007.

[1]   D. Guo and X. Wang, “Quasi-monte carlo filtering in nonlinear dynamic systems,” IEEE Trans. Signal Process, Vol. 54, No. 6, pp. 2087–2098, 2006.

[2]   M. S. Arulampalam, S. Maskell, N. Gordon, et al. “A tutorial on particle filters for online nonlinear/non- gaussian bayesian tracking [J],” IEEE Trans on Signal Proceeding, Vol. 20(2), pp. 174–188, 2002.

[3]   D. Crisan and A. Doucet, “A survey of convergence results on particle filtering methods for practitioners [J],” IEEE Trans on Signal Proceeding, Vol. 50(3), pp. 736– 746, 2002.

[4]   B. D. Anderson and J. B. Moore, “Optimal filtering,” Prentice-Hall, New Jersey, 1979.

[5]   X. R. Li and V. P. Jilkov, “Survey of maneuvering target tarcking part I: dynamci models,” in IEEE Trans. Aerospace and Electronic System, Vol. 39, 2003.

[6]   X. R. Li and V. P. Jilkov, “A survey of maneuvering target tracking-part III: measurement models,” in SPIE Conf. on Signal and Data Proceeding of Small Target, 2001.

[7]   M. Coates, “Distributed particle filters for sensor networks,” in Proceeding of 3rd Intl sysmosium on Information Proceeding in sensor networks, Berkely, CA, USA.

[8]   S. J. Julier and J. K. Uhlmann, “A new extension of the Kalman filter to nonlinear systems,” Proceeding of AeroSense: The 11th International Sysmpsium on Aerospace/ Defence Sensing, Simulation and Controls, Orlando, Florida, 1997. Vol. Muti Sensor Fusion, Tracking and Resource Mangement II pp.182–193.

[9]   Y. Shi and R. C. Eberhart “A modified particle swarm optimizer,” In Proceedings of the IEEE International Conference on Evolutionary Computation. Piscataway, NJ: IEEE Press, pp. 69–73, 1998.

[10]   J. Riget, “A diversity-guided particle swarm optimizer,” the ARPSO. EVALife Technical Report 2002–02, Dept. of Computer Science, University of Arhus, 2002.

[11]   D. Guo, X. Wang, and R. Chen, “New sequential monte carlo methods for nonlinear dynamic systems,” Statistics and Computing, Vol. 15, No. 2, pp. 135–147, 2005.

[12]   Y. Bar-Shalom and X. R. Li, “Kirubarajan T. Estimation with applications to tracking and navigation: theory, algorithm and software [M],” New York: Wiley, 2001.