Extracting Targets State from Particle Approximation of the PHD

Tang Xu; Wei  Ping; Chen  Xin

doi:10.3724/SP.J.1146.2009.01580

Volume 32 Issue 11

Dec. 2010

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Tang Xu, Wei Ping, Chen Xin. Extracting Targets State from Particle Approximation of the PHD[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2691-2694. doi: 10.3724/SP.J.1146.2009.01580

Citation:

Tang Xu, Wei Ping, Chen Xin. Extracting Targets State from Particle Approximation of the PHD[J]. Journal of Electronics & Information Technology, 2010, 32(11): 2691-2694. doi: 10.3724/SP.J.1146.2009.01580

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Extracting Targets State from Particle Approximation of the PHD

doi: 10.3724/SP.J.1146.2009.01580

Tang Xu^{① 魏平
,},
Wei Ping,
Chen Xin

Received Date: 2009-12-11
Rev Recd Date: 2010-05-04
Publish Date: 2010-11-19

Abstract

Abstract

Probability Hypothesis Density (PHD) filter has emerged as one of powerful tools for multi-target tracking. In the Sequential Monte Carlo (SMC) implementation of it, the filters output is particle approximation of PHD, so some special algorithm is needed to extract the target states from those particles. In this paper, an improved algorithm is proposed. Firstly particles are clustered by their positions using the k-means algorithm, and then the positions with maximum of particles weight are searched and estimated in each cluster as the targets positions. Because the information of both particles weight and spatial distribution are utilized, confirmed by simulation results, the new algorithm can provide estimation of the targets states more accurately.
- Multi-target tracking,
- Bayes filtering,
- Particle filter,
- Probability Hypothesis Density (PHD),
- Clustering

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References(1)

References

Mahler R. Statistical Multisource-Multitarget Information Fusion[M]. Artech House, Boston, 2007: 711-715.[2]Ba-ngu vo, Singh S, and Doucet A. Sequential monte carlo methods for multi-target filtering with random finite sets[J].IEEE Transactions on Aerospace and Electronic Systems.2005, 41(4):1224-1245[3]Tobias M and Lanterman A D. Probability hypothesis density-based multitarget tracking with bistatic range and doppler observations[J].IET, Radar, Sonar and Navigation.2005, 152(3):195-205[4]Jain A K, Murty M N, and Flynn P J. Data clustering: a review[J].ACM Computing Surveys.1999, 31(3):264-323[5]Ba-ngu vo and Wing-kin MA. The gaussian mixture probability hypothesis density filter[J].IEEE Transactions on Signal Processing.2006, 54(11):4091-4104[6]Tobias M and Lanterman A D. Techniques for birth-particle placement in the probability hypothesis density particle filter applied to passive radar[J].IET, Radar, Sonar and Navigation.2008, 2(5):351-365[7]Hoffman J and Mahler R. Multitarget miss distance via optimal assignment[J].IEEE Transactions on Systems, Man and Cybernetics-Part A.2004, 34(3):327-336[8]Mahler R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.[9]Clark D, Ristic B, and Ba-ngu Vo. PHD Filtering with target amplitude feature[C]. 11th International Conference on Information Fusion. Cologne, Germany, Jun. 30-July 3, 2008: 1-7.[10]Streit R L. PHD intensity filtering is one step of a MAP estimation algorithm for positron emission tomography[C]. Proc of the International Conference on Information Fusion, Seattle, WA, July 6-9, 2009: 308-315.

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