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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2013-05-13
Cited: 0
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A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
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Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi. A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order[J]. Journal of Zhejiang University Science C, 2013, 14(6): 417-424.
@article{title="A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order",
author="Shi-cang Zhang, Jian-xun Li, Liang-bin Wu, Chang-hai Shi",
journal="Journal of Zhejiang University Science C",
volume="14",
number="6",
pages="417-424",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1200310"
}
%0 Journal Article
%T A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
%A Shi-cang Zhang
%A Jian-xun Li
%A Liang-bin Wu
%A Chang-hai Shi
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 6
%P 417-424
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200310
TY - JOUR
T1 - A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order
A1 - Shi-cang Zhang
A1 - Jian-xun Li
A1 - Liang-bin Wu
A1 - Chang-hai Shi
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 6
SP - 417
EP - 424
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1200310
Abstract: We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density (PHD) filter. First, a variation of the generalized pseudo-Bayesian estimator of first order (VGPB1) is designed to adapt to the gaussian mixture PHD filter for jump Markov system models (JMS-PHD). The probability of each kinematic model, which is used in the JMS-PHD filter, is updated with VGPB1. The weighted sum of state, associated covariance, and weights for Gaussian components are then calculated. Pruning and merging techniques are also adopted in this algorithm to increase efficiency. Performance of the proposed algorithm is compared with that of the JMS-PHD filter. Monte-Carlo simulation results demonstrate that the optimal subpattern assignment (OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.
[1]Bar-Shalom, Y., Chang, K.C., Blom, H.A.P., 1989. Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm. IEEE Trans. Aerosp. Electron. Syst., 25(2):296-300.
[2]Bar-Shalom, Y., Li, X.R., Kirubarajan, T., 2001. Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc., New York, USA.
[3]Blom, H.A.P., Bar-Shalom, Y., 1988. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Automatic Control, 33(8):780-783.
[4]Li, X.R., Jilkov, V.P., 2003. Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans. Aerosp. Electron. Syst., 39(4):1333-1364.
[5]Mahler, R.P.S., 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst., 39(4):1152-1178.
[6]Mahler, R.P.S., 2007. Statistical Multisource Multitarget Information Fusion. Artech House, Norwood, MA.
[7]Pasha, S.A., Vo, B.N., Tuan, H.D., Ma, W.K., 2009. A Gaussian mixture PHD filter for jump Markov system models. IEEE Trans. Aerosp. Electron. Syst., 45(3):919-936.
[8]Pollard, E., Pannetier, B., Rombaut, M., 2011. Hybrid algorithms for multitarget tracking using MHT and GM-CPHD. IEEE Trans. Aerosp. Electron. Syst., 47(2):832-847.
[9]Schuhmacher, D., Vo, B.T., Vo, B.N., 2008. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process., 56(8):3447-3457.
[10]Vo, B.N., Ma, W.K., 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process., 54(11):4091-4104.
[11]Vo, B.N., Pasha, A., Tuan, H.D., 2006. A Gaussian Mixture PHD Filter for Nonlinear Jump Markov Models. Proc. 45th IEEE Conf. on Decision and Control, p.3162-3167.
[12]Vo, B.N., Vo, B.T., Mahler, R.P.S., 2012. Closed-form solutions to forward-backward smoothing. IEEE Trans. Signal Process., 60(1):2-17.
[13]Wu, J., Hu, S., Wang, Y., 2010. Probability-hypothesis-density filter for multitarget visual tracking with trajectory recognition. Opt. Eng., 49(12):129701.
[14]Zhang, H., Jing, Z., Hu, S., 2009. Gaussian mixture CPHD filter with gating technique. Signal Process., 89(8):1521-1530.
[15]Zhang, S.C., Li, J.X., Wu, L.B., 2012. An Improvement to the Linear Jump Markov System Gaussian Mixture Probability Hypothesis Density Filter for Maneuvering Target Tracking. 7th IEEE Conf. on Industrial Electronics and Applications, p.1810-1815.
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