Anti-bias Track Association Algorithm Based on t-distribution Mixture Model

LI Baozhu; DONG Yunlong; LI Xiuyou; GUAN Jian

doi:10.11999/JEIT161084

Volume 39 Issue 7

Jul. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(7): 1774-1778

LI Baozhu, DONG Yunlong, LI Xiuyou, GUAN Jian. Anti-bias Track Association Algorithm Based on t-distribution Mixture Model[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1774-1778. doi: 10.11999/JEIT161084

Citation:

LI Baozhu, DONG Yunlong, LI Xiuyou, GUAN Jian. Anti-bias Track Association Algorithm Based on t-distribution Mixture Model[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1774-1778. doi: 10.11999/JEIT161084

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Anti-bias Track Association Algorithm Based on t-distribution Mixture Model

doi: 10.11999/JEIT161084

Funds:

The National Natural Science Foundation of China (61471382, 61401495, 61501487, 61531020), The Natural Science Foundation of Shandong Province (2015ZRA06052)

Received Date: 2016-10-14
Rev Recd Date: 2017-02-17
Publish Date: 2017-07-19

Abstract

Abstract

In order to solve the problem of robust track-to-track association in the presence of sensor biases and non-identical observation, an anti-bias track association algorithm based on t-distribution mixture model is proposed. The robust track-to-track association problem is turned into the non-rigid point matching problem. The tracks of non-common are regarded as outliers in the point matching for the effects of the track-to-track association caused by non-identical observation. The heavy-tailed t-distribution mixture model is established with better robustness to outliers. The closed-form solution of t-distribution mixture model is solved by Expectation Maximization (EM) algorithm. The conditional expectation function is added a regular item of point set, so that the points have a feature of Coherent Point Drift (CPD). Finally, the effectiveness of the proposed algorithm is verified by simulation experiments at the presence of sensor biases and missed detections.
- Track association,
- Sensor biases,
- t-distribution mixture model,
- Expectation Maximization (EM) algorithm,
- Coherent Point Drift (CPD)

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References

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