In this paper, a novel algorithm named multi-scan mixture particle filter is proposed for joint detection and tracking for a varying number of targets. The posterior distribution of multiple target state in a single-target state space is a multi-mode distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted in this work, which consists of existing components and new components. The new components are generated at each time step to capture the new modes due to newly appeared targets or clutter. In order to distinguish targets from clutter, multiple scan information is incorporated. The history of each component's associate weights is stored in a multi-scan sliding window, which is used to judge whether the component is from a target or clutter. Moreover, a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step. From the simulation results, it could be seen that the proposed algorithm can effectively detect the appearance/disappearance of the targets as well as track the existing target.
2. Hong, , S., L. Wang, Z.-G. Shi, and K. S. Chen, "Simplified particle PHD filter for multiple-target tracking: Algorithm and architecture," Progress In Electromagnetics Research, Vol. 120, 481-498, 2011.
3. Wang, , X., J.-F. Chen, Z.-G. Shi, and K. S. Chen, "Fuzzy-control-based particle filter for maneuvering target tracking," Progress In Electromagnetics Research , Vol. 118, 1-15, 2011.
4. Chen, , J.-F., Z.-G. Shi, S.-H. Hong, and K. S. Chen, "Grey prediction based particle ¯lter for maneuvering target tracking," Progress In Electromagnetics Research, Vol. 93, 237-254, 2009.
5. Li, , Y., Y. J. Gu, Z.-G. Shi, and K. S. Chen, "Li, Y., Y. J. Gu, Z.-G. Shi, and K. S. Chen, \Robust adaptive beamforming based on particle filter with noise unknown," Progress In Electromagnetics Research, Vol. 90, 151-169, 2009.
6. Shi, , Z.-G., S.-H. Hong, and K. S. Chen, "Tracking airborne targets hidden in blind doppler using current statistical model particle filter," Progress In Electromagnetics Research , Vol. 82, 227-240, 2008.
7. Oh, , S., S. Russell, and S. Sastry, "Markov chain Monte Carlo data association for multi-target tracking," IEEE Transactions on Automatic Control, Vol. 54, No. 3, 481-497, 2009.
8. Reid, , D., "An algorithm for tracking multiple targets," IEEE Transactions on Automatic Control, Vol. 24, 84-90, 1979.
9. Bar-Shalom, Y. and T. Fortmann, "Tracking and Data Association," Academic Press, 1988.
10. Kastella, , K. D., "A maximum likelihood estimator for report-to-track association," Proc. SPIE, , 386-393, 1993.