Abstract
Multitarget tracking (MTT) is a frequent topic in visual surveillance systems. Although the multiple-model probability hypothesis density (MM-PHD) filter plays an important role in the MTT, both computerized intractability and imprecise estimate are still inevitable. To solve the problems, a novel filter is presented in this paper. Different from the previous work, the Rao-Blackwellized particle filtering algorithm is incorporated with the MM-PHD filter to reduce computational load, where the sequence Monte Carlo method is adopted to estimate the nonlinear state of targets, and the linear state is predicted using the Kalman filter with the information embedded in the estimated nonlinear state. With respect to tracking precision, we find that the reweighting scheme can be realized for the numberestimate of both undetected targets and false alarms. The result is useful in balancing the required particle number in order to stabilize target estimates during the surveillance period. The illustrative simulation is finally provided to show the effectiveness of the proposed filter.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
R. Mahler, “PHD filters for nonstandard target,” Proc. of the 12th Conf. on Information Fusion, pp. 915–921, 2009.
W. L. Li, Y. M. Jia, J. P. Du, and F. S. Yu, “Gaussian mixture PHD smoother for jump Markov models in multiple maneuvering targets tracking,” Proc. of American Control Conf., pp. 3025–3029, 2011.
K. Panta, D. E. Clark, and B. N. Vo, “Data association and track management for the Gaussian mixture probability hypothesis density filter,” IEEE Trans. on Aerospace and Electronic Systems, vol. 45, no. 3, pp. 1003–1016, July 2009.
R. Mahler, Statistical Multisource-multitarget Information Fusion, Artech House, Norwood, 2007.
B. N. Vo and W. K. Ma, “The Gaussian mixture probability hypothesis density filter,” IEEE Trans. on Signal Processing, vol. 5, no. 11, pp. 3291–3304, November 2006.
R. Mahler, “PHD filters of higher order in target number,” IEEE Trans. on Aerospace and Electronic Systems, vol. 43, no. 4, pp. 1532–1543, October 2007.
C. Ma, Y. San, and Y. Zhu, “Multiple model truncated particle filter for maneuvering target tracking,” Proc. of the 32nd Chinese Control Conf., pp. 4773–4777, 2013.
M. Daniel, R. Stephan, W. Benjamin, and D. Klaus, “Road user tracking using a dempster-shafer based classifying multiple-model PHD filter,” Proc. of the 16th Int. Conf. on Information Fusion, pp. 1236–1242, 2013.
S. H. Hong, Z. G. Shi, and K. S. Chen, “Novel multiple- model probability hypothesis density filter for multiple maneuvering targets tracking,” Proc. of Asia Pacific Conf. on Postgraduate Research in Microelectronics and Electronics, pp. 189–192, 2009.
A. Pasha, B. N. Vo, H. D. Tuan, and W. K. Ma, “A Gaussian mixture PHD filter for jump Markov systems models,” IEEE Trans. on Aerospace and Electronic Systems, vol. 45, no. 3, pp. 919–936, July 2009.
K. Punithakumar, T. Kirubarajan, and A. Sinha, “Multiple-model probability hypothesis density filter for tracking maneuvering targets,” IEEE Trans. on Aerospace and Electronic Systems, vol. 44, no. 1, pp. 87–98, January 2008.
N. Nadarajan, T. Kirubarajan, T. Lang, M. Mcdonald, and K. Punithakumar, “Multitarget tracking using probability hypothesis density smoothing,” IEEE Trans. on Aerospace and Electronic Systems, vol. 47, no. 4, pp. 2344–2360, October 2011.
X. Lin, L. H. Zhu, and Y. Wang, “Improved probability hypothesis density filter for multi-target tracking,” Control and Decision, vol. 26, no. 9, pp. 1367–1372, September 2011.
X.Wang and C. Z. Han, “An improved multiple model GM-PHD filter for maneuvering target tracking,” Chinese Journal of Aeronautics, vol. 26, no. 1, pp. 179–185, January 2013.
O. Erdinc, P. Willett, and Y. Bar-Shalon, “The binoccupancy filter and its connection to the PHD filters,” IEEE Trans. on Signal Processing, vol. 57, no. 11, pp. 4232–4276, November 2009.
R. Mahler, “Approximate multisensor CPHD and PHD filters,” Proc. of the 13th Conf. on Information Fusion, pp. 1–8, 2010.
C. Ouyang, H. B. Ji, and Z. Q. Guo, “Improved multiple model particle PHD and CPHD filters,” Acta Automatica Sinica, vol. 38, no. 3, pp. 341–348, March 2012.
Z. Y. Zhu and X. Q. Dai, “Marginalized particle filter for maneuver target tracking,” Journal of Wuhan University of Technology, vol. 30, no. 6, pp. 118–121, June 2008.
S. C. Zhang, J. X. Li, and L. B. Wu, “A novel multiple maneuvering targets tracking algorithm with data association and track management,” International Journal of Control, Automation and Systems, vol. 11, no. 5, pp. 947–956, October 2013.
Y. J. Liu, S. C. Tong, W. Wang, and Y. M. Li, “Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications,” International Journal of Control, Automation and Systems, vol. 7, no. 4, pp. 681–690, August 2009.
X. Y. Xu and B. X. Li, “Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance,” IEEE Trans. on Image Processing, vol. 16, no. 3, pp. 838–849, March 2007.
T. Schon and F. Gustafsson, “Marginalized particle filters for mixed linear/nonlinear state-space models,” IEEE Trans. on Signal Processing, vol. 53, no. 7, pp. 2279–2289, July 2005.
J. J. Yin, J. Q. Zhang, and K. Mike, “The marginal Rao-Blackwellized particle filter for mixed linear/ nonlinear state space models,” Chinese Journal of Aeronautics, vol. 20, no. 4, pp. 348–354, August 2007.
Z. S. Zhuang, J. Q. Zhang, and J. J. Yin, “Rao-Blackwellized particle probability hypothesis density filter,” Acta Aeronautica et Astronautica Sinica, vol. 30, no. 4, pp. 698–705, April 2009.
K. Panta, Multi-target Tracking Using 1st Moment of Random Finite Sets, The University of Melbourne, Melbourne, 2007.
Y. Lin, Y. Barshalom, and T. Kirubarajan, “Track labeling and PHD filter for multitarget tracking,” IEEE Trans. on Aerospace and Electronic Systems, vol. 42, no. 3, pp. 778–795, July 2006.
B. T. Vo, Random Finite Sets in Multi-objective Filtering, The University of Western Australia, Perth, 2008.
Author information
Authors and Affiliations
Corresponding author
Additional information
Bo Li received his B.S. degree in Communication and Information Systems from Liaoning University of Technology, China in 2005. He is currently an associate professor in Liaoning University of Technology, China, and is pursuing a Ph.D. at College of Information Science and Technology, Dalian Maritime University, China. His research interests include signal processing, communication systems, state estimates and information fusion.
Rights and permissions
About this article
Cite this article
Li, B. Multiple-model Rao-Blackwellized particle probability hypothesis density filter for multitarget tracking. Int. J. Control Autom. Syst. 13, 426–433 (2015). https://doi.org/10.1007/s12555-014-0148-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-014-0148-7