Volume 36, Number 3, June 2018
|Page(s)||420 - 425|
|Published online||08 October 2018|
Extended Target GMPHD Filter Based on Mean Shift and Graph Structure
基于 mean shift 和图结构的 GMPHD 扩展目标跟踪
In view of excessive measurements partition number, a large computation load of extended target tracking and leakage estimation when the extended targets cross, an extended target tracking algorithm based on GMPHD with mean shift and graph structure is proposed. Firstly, the kernel density estimation is used to eliminate the clutter measurements. Secondly, mean shift algorithm is adopted to divide the extended target measurements set, and sub-division is considered to carry or not based on the information fed back from the updated graph structure. Then, the extended target GMPHD algorithm is used to filter. Finally, the graph structure is updated by the one-step predicted value of the filtering result, and the updated graph structure information is used to guide the measurement partition at the next moment. Matlab simulation shows that the algorithm proposed decreases largely the number of measurements partition, reduces the computational complexity, and solves the leakage estimation problem when the targets cross.
针对当前扩展目标跟踪算法中，量测划分数过多、计算量过大，目标交叉时刻易产生漏估等问题，提出一种基于 mean shift 和图结构的 GMPHD 扩展目标跟踪算法。首先，引入核密度估计剔除杂波量测；其次，采用 mean shift 算法对扩展目标量测集进行划分，并依据图结构更新后反馈回的信息判断是否需要进行子划分；然后，采用扩展目标 GMPHD 算法进行滤波处理；最后，对滤波结果进行一步预测，更新图结构，并使用更新后的图结构信息指导下一时刻的量测划分。 matlab 仿真表明，所提算法大幅减少了量测划分数，降低了运算量，解决了扩展目标交叉时刻的漏估问题。
Key words: extended target tracking / mean shift / graph structure / GMPHD / measurements partition / computational efficiency / matlab
关键字 : 扩展目标跟踪 / mean shift / 图结构 / GMPHD / 量测划分 / 计算效率 / matlab
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
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