Track-to-Track Association by Coherent Point Drift

被引:21
|
作者
Zhu, Hao [1 ]
Wang, Mingliang [1 ]
Yuen, Ka-Veng [2 ]
Leung, Henry [3 ]
机构
[1] Chongqing Univ Posts & Telecommun, Automot Elect & Embedded Syst Engn Res Ctr, Dept Automat, Chongqing 400065, Peoples R China
[2] Univ Macau, Fac Sci & Technol, Macau, Peoples R China
[3] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB T2N 1N4, Canada
基金
中国国家自然科学基金;
关键词
Coherent point drift (CPD); Gaussian mixture model (GMM); sensor bias; track-to-track association; REGISTRATION; FUSION; ALGORITHMS;
D O I
10.1109/LSP.2017.2682857
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this letter, we propose a probabilistic method, called the coherent point drift (CPD) algorithm, to address track-to-track association with sensor bias. In the CPD method for a pair of sensors, the local tracks of one sensor are represented by Gaussian mixture model centroids, and the local tracks of the other sensor are fitted to those of the first sensor by maximizing the likelihood. An expectation-maximization algorithm is proposed to find the correspondence matrix between the local tracks. Experiments illustrate the effectiveness of our method.
引用
收藏
页码:643 / 647
页数:5
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