高斯过程回归模型多扩展目标多伯努利滤波器
A multiple extended target multi-Bernouli filter based on Gaussian process regression model
摘要点击 99  全文点击 39  投稿时间:2019-11-27  修订日期:2020-03-16
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DOI编号  10.7641/CTA.2020.90978
  2020,37(9):1931-1943
中文关键词  多扩展目标跟踪  随机超曲面  高斯过程回归  随机有限集  多伯努利滤波器
英文关键词  multiple extended target tracking  random hypersurface  Gaussian process regression  random finite set  multi-Bernoulli filter
基金项目  国家国防基础科研项目(JCKY2018427C002), 国家自然科学基金项目(61873116, 51668039, 61763029), 甘肃省科技计划项目(18JR3RA137)资助
作者单位E-mail
陈辉 兰州理工大学 huich78@hotmail.com 
李国财 兰州理工大学  
韩崇昭 西安交通大学  
杜金瑞 兰州理工大学  
中文摘要
      针对复杂不确定性环境下不规则形状的多扩展目标跟踪问题, 本文提出了一种基于高斯过程回归(GPR) 模型的多扩展目标多伯努利(GPR–ETCBMeMBer)滤波算法. 首先, 在利用有限集统计理论(FISST)将多扩展目标的 状态集与量测集分别建模为多伯努利随机有限集(MBer RFS) 和泊松随机有限集(Poisson RFS) 的基础上, 通过 GPR方法建立多扩展目标随机超曲面的跟踪滤波模型. 然后, 基于容积卡尔曼滤波器(CKF)详细推导并提出GPR多 扩展目标多伯努利滤波算法的高斯混合(GM)实现. 最后, 通过构造对星凸形多扩展目标和多群目标跟踪的仿真实 验验证了本文所提算法的有效性.
英文摘要
      In view of the tracking problem of multiple extended target with irregular shape in the complicated and uncertain environment, a Gaussian process regression (GPR) based multiple extended target multi-Bernoulli filter (GPR– ETCBMeMBer) algorithm is proposed in this article. Firstly, on the basis of modeling state set and measurement set of multiple extended target as multi-Bernoulli random finite set (MBer RFS) and Poisson RFS respectively by using finite set statistics (FISST), This article models the random hypersurface based filtering algorithm of multiple extended target via GPR approach. Then, this article derives in detail and proposes a Gaussian mixture (GM) implementation of the GPR– ETCBMeMBer filter via the cubature Kalman filter (CKF). Finally, the effectiveness of the proposed method is verified by the simulations of star-convex shape multiple extended target tracking and multiple group target tracking.