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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-5dac4f8c-966b-477d-935f-06cf108baa35

Czasopismo

International Journal of Electronics and Telecommunications

Tytuł artykułu

Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

Autorzy Zhang, H.  Ge, H.  Yang, J. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN 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.
Słowa kluczowe
EN closely spaced targets   random finite set   probability hypothesis density filter   Gaussian mixture PHD   weight redistribution  
Wydawca Polish Academy of Sciences, Committee of Electronics and Telecommunication
Czasopismo International Journal of Electronics and Telecommunications
Rocznik 2017
Tom Vol. 63, No. 3
Strony 247--254
Opis fizyczny Bibliogr. 17 poz., wykr.
Twórcy
autor Zhang, H.
  • School of Electronic & Electrical Engineering, Shangqiu Normal University, China, hsj_hqzhang@126.com
  • School of Internet of Things Engineering, Jiangnan University, China
autor Ge, H.
autor Yang, J.
Bibliografia
[1] R. Mahler, Statistical multisource multitarget information fusion, Artech House, Norwood, MA, 2007.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] B. Li, F.W. Pang, Improved cardinalized probability hypothesis density filtering algorithm, Applied Soft Computing, Vol.24, 2014, pp.692-703.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-5dac4f8c-966b-477d-935f-06cf108baa35
Identyfikatory
DOI 10.1515/eletel-2017-0033