Extended Target Tracking Method Based on Multi-BernoulliProbability Hypothesis Density

LI Wenjuan; GU Hong; SU Weimin

doi:10.11999/JEIT160372

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3114-3121

LI Wenjuan, GU Hong, SU Weimin. Extended Target Tracking Method Based on Multi-BernoulliProbability Hypothesis Density[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3114-3121. doi: 10.11999/JEIT160372

Citation:

LI Wenjuan, GU Hong, SU Weimin. Extended Target Tracking Method Based on Multi-BernoulliProbability Hypothesis Density[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3114-3121. doi: 10.11999/JEIT160372

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Extended Target Tracking Method Based on Multi-BernoulliProbability Hypothesis Density

doi: 10.11999/JEIT160372

LI Wenjuan¹,
GU Hong^{1
,
,},
SU Weimin¹

Funds:

The National Natural Science Foundation of China (61471198)

Received Date: 2016-04-18
Rev Recd Date: 2016-08-25
Publish Date: 2016-12-19

Abstract

Abstract

Extended targets usually generate multiple measurements in high resolution radar systems. Existing algorithms of the Random Finite Set (RFS) assume that the measurement number of extended targets follows Poisson distribution in a general way. However, this assumption is inconsistent with actual situations. Considering this issue, a Multi-Bernoulli Extended Target Probability Hypothesis Density (MB-ET-PHD) tracking method is proposed. First, this method assumes that the measurement number of extended targets is Multi-Bernoulli (MB) distributed. Then, its update equation is derived by using the FInite Set STatistics (FISST) multi-target calculus. Finally, simulated results of Gaussian Mixture (GM) framework are given. The simulation results show that the proposed method can obtain better tracking performance compared with the Poisson ET-PHD method.
- Extended target tracking,
- Probability Hypothesis Density (PHD),
- Multi-Bernoulli (MB)

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References(20)

References

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