扩展目标多特征估计自适应渐进滤波器

王旭昕; 陈辉; 连峰; 张光华

doi:10.12263/DZXB.20230873

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扩展目标多特征估计自适应渐进滤波器

学术论文 | 更新时间：2025-12-08

- 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
- 电子学报 2024年52卷第9期页码：3135-3147
- 作者机构：
  
  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
- 作者简介：
  
  [ "王旭昕女，1997年12月出生于甘肃省定西市.现为兰州理工大学电气工程与信息工程学院博士研究生.主要研究方向为最优滤波，目标跟踪等. E-mail: wxxin@lut.edu.cn" ]
  [ "陈辉男，1978年11月出生于甘肃省兰州市.现为兰州理工大学电气工程与信息工程学院教授、博士生导师.主要研究方向为多目标跟踪，传感器管理，最优控制等. E-mail: huich78@hotmail.com" ]
  [ "连峰男，1981年9月出生于陕西省宝鸡市.现为西安交通大学自动化科学与工程学院教授，博士生导师.主要研究方向为目标跟踪，信息跟踪与传感器管理，最优控制等. E-mail: lianfeng1981@mail.xjtu.edu.cn" ]
  张光华男，1988年10月出生于河北省张家口市.现为西安交通大学自动化科学与工程学院副教授.主要研究方向为信息融合与目标跟踪.E-mail: MichaelZgh@stu.xjtu.edu.cn
- 基金信息：
  
  国家自然科学基金(62163023;62173266;62103318;61873116);甘肃省教育厅产业支撑计划(2021CYZC-02);2023年甘肃省军民融合发展专项资金项目
- DOI：10.12263/DZXB.20230873
  中图分类号： TP274
- 收稿：2023-09-19，
  
  修回：2024-03-01，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
王旭昕, 陈辉, 连峰, 等. 扩展目标多特征估计自适应渐进滤波器[J]. 电子学报, 2024, 52(09): 3135-3147.

WANG Xu-xin, CHEN Hui, LIAN Feng, et al. Extended Target Multi-Feature Estimation Adaptive Progressive Filter[J]. Acta Electronica Sinica, 2024, 52(09): 3135-3147.
王旭昕, 陈辉, 连峰, 等. 扩展目标多特征估计自适应渐进滤波器[J]. 电子学报, 2024, 52(09): 3135-3147. DOI：10.12263/DZXB.20230873

WANG Xu-xin, CHEN Hui, LIAN Feng, et al. Extended Target Multi-Feature Estimation Adaptive Progressive Filter[J]. Acta Electronica Sinica, 2024, 52(09): 3135-3147. DOI：10.12263/DZXB.20230873

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，RHM-APBF）.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.

Abstract

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 distribution 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 progressive update with adaptive variable step size. Furthermore

the random hypersurface model is used to represent the measurement 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 simulation experiments of the extended target (ET) and group target (GT) at different noise level and complex random environment.

关键词

Keywords

references

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