Blending Sensor Scheduling Strategy with Particle Filter to Track a Smart Target

DOI: 10.4236/wsn.2009.14037   PDF   HTML     5,065 Downloads   8,729 Views   Citations


We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kal-man filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.

Share and Cite:

B. LIU, C. JI, Y. ZHANG and C. HAO, "Blending Sensor Scheduling Strategy with Particle Filter to Track a Smart Target," Wireless Sensor Network, Vol. 1 No. 4, 2009, pp. 300-305. doi: 10.4236/wsn.2009.14037.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. Kreucher, D. Blatt, A. Hero, and K. Kastella, “Adaptive multi-modality sensor scheduling for detection and tracking of smart targets,” Digital Signal Process, Vol. 16, pp. 546–567, 2006.
[2] C. Savage and B. L. Scala, “Sensor management for tracking smart targets,” Digital Signal Process, doi:10.1016/j.dsp.2007.10.013 , 2007.
[3] J. C. Gittins and D. M. Roberts, “Search for an intelligent evader concealed in one of an arbitrary number of regions,” Naval Research Logistics Quarterly, Vol. 26, No. 4, pp. 657–666, 1979.
[4] D. M. Roberts and J. C. Gittins, “Search for an intelligent evader: strategies for searcher and evader in the two-region problem,” Naval Research Logistics Quarterly, Vol. 25, No.1, pp. 95–106, 1978.
[5] B. Liu, X. Ma, and C. Hou, “Smart target tracking using sensor scheduling and particle filter,” in Proc. of Inter. Conf. on Signal Processing, Beijing, pp. 2620– 2623, 2008.
[6] N. Xiong and P. Svensson, “Multi-sensor management for in-formation fusion: Issues and approaches,” Information Fusion, Vol. 3, No. 2, pp. 163–186, 2002.
[7] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, 2004.
[8] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonliear/non- gaussian bayesian tracking,” IEEE Trans. on Signal Process, Vol. 50, No. 2, pp. 174–188, 2002.
[9] A. Doucet, N. De. Freitas, and N. Gordon, Sequential Monte Carlo in Practice, Springer Verlag, New York, 2001.
[10] M. Kalandros and L. Y. PAO, “Covariance control for multisen-sor systems,” IEEE Trans. on Aerospace and Electronics Systems, Vol. 38, No. 4, pp. 1138–1157, 2002.
[11] M. Briers, S. Maskell, and R. Wright, “A rao-blackwellised unscented kalman ?lter,” in Proc. of the 6th Int. Conf of Info. Fusion, Vol. 1, pp. 55–61, 2003.
[12] R. der Merwe, A. Doucet, N. Freitas, and E. Wan, “The unscented particle filter,” Tech. Rep, Department of en-gineering, University of Cambridge, CB21PZ Cambridge, 2000.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.