高斯过程回归的CPHD扩展目标跟踪

doi:10.3969/j.issn.1001-2400.2017.03.002

西安电子科技大学学报

高斯过程回归的CPHD扩展目标跟踪

李翠芸1;王精毅1,2;姬红兵1

(1. 西安电子科技大学电子工程学院，陕西西安 710071;
2. 中国人民解放军95980部队，湖北襄阳 441000)

收稿日期:2016-05-06 出版日期:2017-06-20 发布日期:2017-07-17
作者简介:李翠芸(1976-)，女，副教授，博士，E-mail: cyli@xidian.edu.cn
基金资助:
国家自然科学基金资助项目(61372003)；国家自然科学基金青年基金资助项目(61301289)

Extended target tracking based on CPHD with Gaussian process regression

LI Cuiyun1;WANG Jingyi1,2;JI Hongbing1

(1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
2. Unit 95980 of PLA, Xiangyang 441000, China)

Received:2016-05-06 Online:2017-06-20 Published:2017-07-17

摘要/Abstract

摘要：

针对现有扩展目标跟踪算法中，形状估计复杂，在考虑漏检及杂波情况下目标跟踪精度不高等问题，提出了一种基于高斯过程回归的伽玛高斯混合势概率假设密度扩展目标跟踪算法．该算法采用星凸模型对目标进行建模，在伽玛高斯混合势概率假设密度滤波器对扩展目标运动状态估计良好的基础上，利用高斯过程回归对目标形状进行估计，实现了对扩展目标的有效跟踪．实验仿真表明，所提算法能够对目标的运动状态进行高效跟踪，且在扩展形状的估计精度、计算速度等方面要优于基于星凸随机超曲面的伽玛高斯混合势概率假设密度滤波器．

关键词: 星凸模型, 高斯过程回归, 势概率假设密度, 形状估计

Abstract:

In view of the complexity of estimating the shape of extended targets and the low accuracy in multiple extended target tracking in the clutters and missed detections, a Gamma Gaussian-mixture cardinalized probability hypothesis density filter with Gaussian Process Regression which can adaptively estimate the shape of the extended targets is proposed. First, the extension of targets is modeled as a star-convex model, and on the basis of good estimation performance for the motion state with the Gamma Gaussian-mixture cardinalized probability hypothesis density filter, the Gaussian Process Regression is used to estimate the shape of extended targets, thus achieving the purpose of tracking the extended target. Simulation shows that the proposed algorithm outperforms the Gamma Gaussian-mixture cardinalized probability hypothesis density filter based on the star convex random hypersurface model in estimation precision and computing speed.

Key words: star-convex models, Gaussian processes regression, cardinalized probability hypothesis density, shape estimation

李翠芸;王精毅;姬红兵. 高斯过程回归的CPHD扩展目标跟踪[J]. 西安电子科技大学学报, 2017, 44(3): 8-12+30.

LI Cuiyun;WANG Jingyi;JI Hongbing. Extended target tracking based on CPHD with Gaussian process regression[J]. Journal of Xidian University, 2017, 44(3): 8-12+30.

参考文献

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[1]	王明杰,姬红兵,刘龙. 噪声野值下的学生t分布混合CPHD滤波[J]. 西安电子科技大学学报, 2019, 46(5): 134-141.
[2]	李翠芸;王精毅;姬红兵. 高斯过程回归下的多机动扩展目标跟踪[J]. 西安电子科技大学学报, 2017, 44(6): 31-36.

高斯过程回归的CPHD扩展目标跟踪

Extended target tracking based on CPHD with Gaussian process regression

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