高斯过程回归的CPHD扩展目标跟踪

doi:10.3969/j.issn.1001-2400.2017.03.002

Abstract

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

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.

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[1]	WANG Mingjie,JI Hongbing,LIU Long. Student’s t Distribution mixture CPHD filter with noise outliers [J]. Journal of Xidian University, 2019, 46(5): 134-141.
[2]	LI Cuiyun;WANG Jingyi;JI Hongbing. Multiple maneuvering extended targets tracking with Gaussian process regression [J]. Journal of Xidian University, 2017, 44(6): 31-36.

Extended target tracking based on CPHD with Gaussian process regression

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