Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains

Jiangyi LIU; Chunping WANG

doi:10.11999/JEIT180352

Volume 41 Issue 2

Jan. 2019

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Jiangyi LIU, Chunping WANG. Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains[J]. Journal of Electronics & Information Technology, 2019, 41(2): 492-497. doi: 10.11999/JEIT180352

Citation:

Jiangyi LIU, Chunping WANG. Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains[J]. Journal of Electronics & Information Technology, 2019, 41(2): 492-497. doi: 10.11999/JEIT180352

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Cardinalized Probability Hypothesis Density Filter Based on Pairwise Markov Chains

doi: 10.11999/JEIT180352

Jiangyi LIU,
Chunping WANG^,

Electronic and optical engineering Department, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China

Received Date: 2018-04-17
Rev Recd Date: 2018-09-10

Available Online: 2018-09-25

Publish Date: 2019-02-01

Abstract

Abstract

In view of the problem that the Cardinalized Probability Hypothesis Density (CPHD) probability hypothesis density filtering algorithm based on the Pairwise Markov Chains (PMC) model (PMC-CPHD) is not suitable for implementation, the PMC-CPHD algorithm is modified into a polynomial form to facilitate implementation, and the Gauss Mixture (GM) implementation of the improved algorithm is given. The experimental results show that the given GM implementation realizes multitarget tracking effectively, and improves the stability of the target number estimation compared with the GM implementation of the probability hypothesis density filtering algorithm based on the PMC model (PMC-PHD).
- Pairwise Markov Chains (PMC),
- Cardinalized Probability Hypothesis Density (CPHD),
- Gauss Mixture (GM)

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References(15)

References

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