西安电子科技大学学报（自然科学版）2017，Vol.44Issue(3)：8-12,30,6.DOI:10.3969/j.issn.1001-2400.2017.03.002
高斯过程回归的CPHD扩展目标跟踪
Extended target tracking based on CPHD with Gaussian process regression
摘要
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 starconvex 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.关键词
星凸模型/高斯过程回归/势概率假设密度/形状估计Key words
star-convex models/Gaussian processes regression/cardinalized probability hypothesis density/shape estimation分类
信息技术与安全科学引用本文复制引用
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（自然科学版）,2017,44(3):8-12,30,6.基金项目
国家自然科学基金资助项目(61372003) （61372003）
国家自然科学基金青年基金资助项目(61301289) （61301289）
