Anti-bias track association algorithm based on Gaussian mixture model

被引：0
|
作者：
Li B. [1 ]
Dong Y. [1 ]
Ding H. [1 ]
Guan J. [1 ]
机构：
[1] Institute of Information Fusion, Naval Aviation University, Yantai
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2019年 / 40卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Expectation Maximization (EM); Gaussian mixture model; Neighbor topology information; Time-varied sensor biases; Track association;
D O I：
10.7527/S1000-6893.2018.22650
中图分类号：
学科分类号：
摘要：
To address the track-to-track association problem in the presence of time-varied sensor biases and different targets reported by different sensors, Gaussian Mixture Model (GMM) and neighborhood topology information are used in this paper. The robust track-to-track association problem is turned into a non-rigid point matching problem. The Gaussian mixture model is established with better robustness to 'unpaired' tracks. The weight of each Gaussian component is decided by the neighborhood topology information between tracks. The optimal closed solution of the Gaussian mixture model is solved by Expectation Maximization (EM) algorithm. In Expectation-step of the EM algorithm the correspondence of tracks is solved, and in Maximization-step the 'unpaired' tracks ratio are calculated. Finally, the track-to-track association is obtained by judgment. Monte carlo simulation demonstrates the effectiveness of the proposed approaches under different sensor biases, targets densities and detection probabilities. © 2019, Press of Chinese Journal of Aeronautics. All right reserved.
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