扩展目标多特征估计自适应渐进滤波器  

Extended Target Multi-Feature Estimation Adaptive Progressive Filter

作　　者：王旭昕 陈辉[1] 连峰[2] 张光华[2] WANG Xu-xin;CHEN Hui;LIAN Feng;ZHANG Guang-hua(School of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China;School of Automation Science and Engineering,Xi'an Jiaotong University,Xi'an,Shaanxi 710049,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,甘肃兰州730050 [2]西安交通大学自动化科学与工程学院,陕西西安710049

出　　处：《电子学报》2024年第9期3135-3147,共13页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62163023,No.62173266,No.62103318,No.61873116);甘肃省教育厅产业支撑计划(No.2021CYZC-02);2023年甘肃省军民融合发展专项资金项目。

摘　　要：针对具有不规则形状的扩展目标跟踪(Extended Target Tracking,ETT)问题,本文提出了一种基于随机超曲面模型的自适应渐进贝叶斯滤波器(Random Hypersurface Model-Adaptive Progressive Bayesian Filter,RHMAPBF).首先,对扩展目标连续状态先验概率密度的局部累积分布进行随机采样,再最小化连续概率密度和狄拉克混合概率密度的局部累积分布之间的修正克莱默冯米塞斯距离得到粒子的最优位置,以自适应地变步长进而渐进更新将粒子迁移到扩展目标后验的密集区域求得更加准确的后验概率密度近似;其次,利用随机超曲面描述任意星凸形扩展目标的量测源分布,提出了星凸形不规则形状扩展目标跟踪自适应渐进滤波器,有效实现了不规则形状扩展目标多特征概率密度信息的递归.最后通过不同噪声水平以及复杂随机环境的扩展目标(Extended Target,ET)和群目标(Group Target,GT)的跟踪仿真实验验证本文方法的有效性.To address the problem of extended target tracking(ETT)with irregular shape,this paper proposes a random hypersurface model-adaptive progressive bayesian filter(RHM-APBF).First,the local cumulative distribution of the continuous state prior probability density of extended target is randomly sampled,and the optimal position of the sampling point is obtained by minimizing the modified Cramer-Von Mises distance between the local cumulative distri⁃bution of the continuous probability density and the Dirac mixture probability density.Then,the sampled particles are migrated to the posterior dense area to obtain a more accurate posterior probability density approximation by progres⁃sive update with adaptive variable step size.Furthermore,the random hypersurface model is used to represent the mea⁃surement source distribution of arbitrary star-convex extended targets,and an adaptive progressive filter for tracking star-convex irregular shape extended target is proposed,which effectively recurses the multi-feature probability density of irregular shape extended targets.Finally,the effectiveness of the proposed method is verified by the tracking simula⁃tion experiments of the extended target(ET)and group target(GT)at different noise level and complex random envi⁃ronment.

关 键 词：扩展目标跟踪 局部累积分布 渐进更新 随机超曲面模型 不规则形状 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]