基于t分布混合模型的抗差关联算法

李保珠; 董云龙; 李秀友; 关键

doi:10.11999/JEIT161084

基于t分布混合模型的抗差关联算法

doi: 10.11999/JEIT161084

基金项目:

国家自然科学基金(61471382, 61401495, 61501487, 61531020)，山东省自然科学基金(2015ZRA06052)

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- 文章访问数: 1317
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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-14
- 修回日期: 2017-02-17
- 刊出日期: 2017-07-19

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

Funds:

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

摘要

摘要: 针对传感器系统误差和观测目标不完全一致的情况下目标航迹关联中鲁棒性问题，该文提出一种基于t分布混合模型的抗差关联算法。将航迹关联问题转化为图像匹配中的非刚性点集匹配问题，针对非共同观测目标影响关联性能的问题，将非共同观测目标的航迹视为图像匹配中的异常点，建立了对异常点具有更好鲁棒性的重拖尾t分布混合模型，利用期望最大化(EM)算法求解t分布混合模型的闭合解，在求解中为了确保航迹点间的运动一致性(CPD)，加入Tikhonov正则项。最后通过实验仿真验证，所提算法在系统误差和观测目标不完全一致情况下的鲁棒性和有效性。
- 航迹关联 /
- 系统误差 /
- t分布混合模型 /
- 期望最大化算法 /
- 运动一致性
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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参考文献(19)

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