Vol. 50
Latest Volume
All Volumes
PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2013-04-17
A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets
By
Progress In Electromagnetics Research B, Vol. 50, 365-381, 2013
Abstract
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.
Citation
Jing Liu Chong Zhao Han Xiang Hua Yao Feng Lian , "A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets," Progress In Electromagnetics Research B, Vol. 50, 365-381, 2013.
doi:10.2528/PIERB13022011
http://www.jpier.org/PIERB/pier.php?paper=13022011
References

1. Shi, , Z.-G., , Y. Zheng, X. Bian, and Z. Yu, "Threshold-based resampling for high-speed particle PHD filter," Progress In Electromagnetics Research, Vol. 136, 369-383, 2013.

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.
doi:10.2528/PIER11051907

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.
doi:10.2528/PIER09042204

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.
doi:10.2528/PIER09010302

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.
doi:10.2528/PIER08012407

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.
doi:10.1109/TAC.2009.2012975

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.
doi:10.1117/12.157806