Novel Track Coalescence Avoiding Joint Integrated Probabilistic Data Association Filter

ZHU Yun; WANG Jun; CHEN Gang; GUO Shuai

doi:10.11999/JEIT170085

Volume 39 Issue 10

Oct. 2017

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ZHU Yun, WANG Jun, CHEN Gang, GUO Shuai. Novel Track Coalescence Avoiding Joint Integrated Probabilistic Data Association Filter[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2346-2353. doi: 10.11999/JEIT170085

Citation:

ZHU Yun, WANG Jun, CHEN Gang, GUO Shuai. Novel Track Coalescence Avoiding Joint Integrated Probabilistic Data Association Filter[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2346-2353. doi: 10.11999/JEIT170085

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Novel Track Coalescence Avoiding Joint Integrated Probabilistic Data Association Filter

doi: 10.11999/JEIT170085 cstr: 32379.14.JEIT170085

Funds:

The National Natural Science Foundation of China (61401526), The Foundation of National Ministries (9140A07020614DZ01)

Received Date: 2017-01-23
Rev Recd Date: 2017-07-17
Publish Date: 2017-10-19

Abstract

Abstract

To avoid the track coalescence of the Joint Integrated Probabilistic Data Association (JIPDA), a modified version of JIPDA is proposed by modelling targets as Random Finite Set (RFS). The JIPDA first generates the original Probability Density Function (PDF) and then makes an approximation of the PDF to estimate target states. To maximize the similarity between the state estimate PDF and the original PDF, the original PDF is optimized when target label is irrelevant. Using the KL divergence as a measure of the similarity, the cost function is developed. The experimental results show that the proposed method can effectively avoid the track coalescence.
- Multi-target tracking,
- Joint Integrated Probabilistic Data Association (JIPDA),
- Random Finite Set (RFS)

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References(18)

References

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