Student’s <i>t</i> Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter

Shuxin CHEN; Lei HONG; Hao WU; Zhuowei LIU; Longhua YUE

doi:10.11999/JEIT181121

Volume 41 Issue 10

Oct. 2019

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Shuxin CHEN, Lei HONG, Hao WU, Zhuowei LIU, Longhua YUE. Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2457-2463. doi: 10.11999/JEIT181121

Citation:

Shuxin CHEN, Lei HONG, Hao WU, Zhuowei LIU, Longhua YUE. Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2457-2463. doi: 10.11999/JEIT181121

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Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter

doi: 10.11999/JEIT181121

1.
Institute of Information and Navigation, Aire Force Engineering University, Xi’an 710077, China
2.
Air Force Research Institute, Beijing 100096, China
3.
Unit 93658, Beijing 100144, China

Funds: The National Natural Science Foundation of China (61703420, 61673392)

Received Date: 2018-12-04
Rev Recd Date: 2019-04-28

Available Online: 2019-05-21

Publish Date: 2019-10-01

Abstract

Abstract

The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods.

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References(14)

References

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