Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

Wu Wei-hua; Jiang Jing; Feng Xun; Liu Chong-yang

doi:10.11999/JEIT141232

Volume 37 Issue 6

Jun. 2015

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Wu Wei-hua, Jiang Jing, Feng Xun, Liu Chong-yang. Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232

Citation:

Wu Wei-hua, Jiang Jing, Feng Xun, Liu Chong-yang. Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232

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Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

doi: 10.11999/JEIT141232

Received Date: 2014-09-23
Rev Recd Date: 2014-12-15
Publish Date: 2015-06-19

Abstract

Abstract

In order to take full advantage of Doppler information for Multi-Target Tracking (MTT) in the clutter environment under the framework of emerging Random Finite Sets (RFS), an MTT algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) for pulse Doppler radar is proposed. Based on the standard GM-CPHD, the target states are updated sequentially using Doppler measurements after updating them using position measurements, then more accurate likelihood function and state estimation are obtained. Simulation results show the effectiveness of the proposed algorithm, and the introduced Doppler information can effectively suppress clutter and evidently improve tracking performance.

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References(16)

References

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