Improved Gaussian mixture probability hypothesis density for tracking closely spaced targets
Abstract
Probability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.References
R. Mahler, Statistical multisource multitarget information fusion, Artech House, Norwood, MA, 2007.
R. Mahler, Multitarget Bayes filtering via first-order multitarget moments, IEEE Transaction on Aerospace and Electronic Systems, Vol.39, No.4, 2003, pp.1152-1178.
R. Mahler, PHD filters of higher order in target number, IEEE Transactions on Aerospace and Electronic Systems, Vol.43, No.4, 2007, pp.1523-1543.
B.N. Vo, S. Singh, A. Doucet, Sequential Monte Carlo implementation of the PHD filter for multi-target tracking, Proc. of the 6th International Conference on Information Fusion, 2003, pp.792-799.
B.N. Vo, W.K. Ma, The Gaussian mixture probability hypothesis density filter, IEEE Transactions on Signal Processing, Vol.54, No.11, 2006, pp.4091-4104.
C. OuYang, H.B. Ji, Y. Tian, Improved Gaussian mixture CPHD tracker for multitarget tracking, IEEE Transaction on Aerospace and Electronic Systems, Vol.49, No.2, 2013, pp.1177-1191.
T.C. Li, S.D. Sun, T.P. Sattar, High-speed Sigma-gating SMC-PHD filter, Signal Processing, Vol.93, No.9, 2013, pp.2586-2593.
B. Li, F.W. Pang, Improved cardinalized probability hypothesis density filtering algorithm, Applied Soft Computing, Vol.24, 2014, pp.692-703.
M. Yazdian-Dehkordi, O.R. Rojhani, Z. Azimifar, Visual target tracking in occlusion condition: A GM-PHD-based approach, Proc. of 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2012, pp. 538-541.
S. Reuter, B.T. Vo, B.N. Vo, et al., The Labeled Multi-Bernoulli Filter, IEEE Transactions on Signal Processing, Vol.62, No.12, 2014, pp:3246-3260.
B.T. Vo, B.N. Vo, Labeled Random Finite Sets and Multi-Object Conjugate Priors, IEEE Transactions on Signal Processing, Vol.61, No.13, 2013, pp.3460-3475.
B.N. Vo, B.T. Vo, D. Phung, Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter, IEEE Transactions on Signal Processing, Vol.62, No.24, 2014, pp.6554-6567.
M. Yazdian-Dehkordi, Z. Azimifar, M.A. Masnadi-Shirazi, An improvement on GM-PHD filter for occluded target tracking, Proc. of the IEEE 36th International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp.1773–1776.
M. Yazdian-Dehkordi, Z.Azimifar, M.A. Masnadi-Shirazi, Penalized Gaussian mixture probability hypothesis density filter for multiple target tracking, Signal Processing, Vol.92, No.5, 2012, pp.1230–1242.
Y. Wang, H.D. Meng, Y.M Liu, et al., Collaborative penalized Gaussian mixture PHD tracker for close target tracking, Signal Processing, Vol.102, 2014, pp:1-15.
M. Yazdian-Dehkordi, Z. Azimifar, M.A. Masnadi-Shirazi, Competitive Gaussian mixture probability hypothesis density filter for multiple target tracking in the presence of ambiguity and occlusion, IET Radar, Sonar & Navigation, Vol.6, No.4, 2012, pp.251-262.
D. Schuhmacher, B.T. Vo, B.N. Vo, A Consistent Metric for Performance Evaluation of Multi-Object Filters, IEEE Transactions on Signal Processing, Vol.56, No.8, 2008, pp.3447-3457.
Downloads
Published
Issue
Section
License
Copyright (c) 2017 International Journal of Electronics and Telecommunications
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.
2. Author’s Warranties
The author warrants that the article is original, written by stated author/s, has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author/s. The undersigned also warrants that the manuscript (or its essential substance) has not been published other than as an abstract or doctorate thesis and has not been submitted for consideration elsewhere, for print, electronic or digital publication.
3. User Rights
Under the Creative Commons Attribution license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions: 1. they must attribute the contribution in the manner specified by the author or licensor, 2. they may alter, transform, or build upon this work, 3. they may use this contribution for commercial purposes.
4. Rights of Authors
Authors retain the following rights:
- copyright, and other proprietary rights relating to the article, such as patent rights,
- the right to use the substance of the article in own future works, including lectures and books,
- the right to reproduce the article for own purposes, provided the copies are not offered for sale,
- the right to self-archive the article
- the right to supervision over the integrity of the content of the work and its fair use.
5. Co-Authorship
If the article was prepared jointly with other authors, the signatory of this form warrants that he/she has been authorized by all co-authors to sign this agreement on their behalf, and agrees to inform his/her co-authors of the terms of this agreement.
6. Termination
This agreement can be terminated by the author or the Journal Owner upon two months’ notice where the other party has materially breached this agreement and failed to remedy such breach within a month of being given the terminating party’s notice requesting such breach to be remedied. No breach or violation of this agreement will cause this agreement or any license granted in it to terminate automatically or affect the definition of the Journal Owner. The author and the Journal Owner may agree to terminate this agreement at any time. This agreement or any license granted in it cannot be terminated otherwise than in accordance with this section 6. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
7. Royalties
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by the Journal Owner or its sublicensee.
8. Miscellaneous
The Journal Owner will publish the article (or have it published) in the Journal if the article’s editorial process is successfully completed and the Journal Owner or its sublicensee has become obligated to have the article published. Where such obligation depends on the payment of a fee, it shall not be deemed to exist until such time as that fee is paid. The Journal Owner may conform the article to a style of punctuation, spelling, capitalization and usage that it deems appropriate. The Journal Owner will be allowed to sublicense the rights that are licensed to it under this agreement. This agreement will be governed by the laws of Poland.
By signing this License, Author(s) warrant(s) that they have the full power to enter into this agreement. This License shall remain in effect throughout the term of copyright in the Work and may not be revoked without the express written consent of both parties.
