采用随机矩阵的多扩展目标滤波器

韩玉兰; 朱洪艳; 韩崇昭

doi:10.7652/xjtuxb201507017

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采用随机矩阵的多扩展目标滤波器

更新时间：2025-07-09

- 采用随机矩阵的多扩展目标滤波器
- A Multi-Target Filter Based on Random Matrix
- 西安交通大学学报 2015年49卷第7期页码：98-104
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2014-12-27。作者简介: 韩玉兰(1982—),女,博士生;朱洪艳(通信作者),女,副教授。基金项目: 国家自然科学基金资助项目(61203220,61221063,61074176,61370037);国家“973计划”资助项目(2013CB329405)
- DOI：10.7652/xjtuxb201507017
  中图分类号： TN274
- 网络首发：2015-07-10，
  
  纸质出版：2015
- 稿件说明：
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韩玉兰, 朱洪艳, 韩崇昭. 采用随机矩阵的多扩展目标滤波器[J]. 西安交通大学学报, 2015,49(7):98-104.

A Multi-Target Filter Based on Random Matrix[J]. 2015, 49(7): 98-104.
韩玉兰, 朱洪艳, 韩崇昭. 采用随机矩阵的多扩展目标滤波器[J]. 西安交通大学学报, 2015,49(7):98-104. DOI： 10.7652/xjtuxb201507017.

A Multi-Target Filter Based on Random Matrix[J]. 2015, 49(7): 98-104. DOI： 10.7652/xjtuxb201507017.

摘要

针对杂波环境下多扩展目标的运动状态和形状信息的联合估计跟踪的问题

提出了一种基于随机矩阵的扩展目标跟踪算法。该算法采用具有噪声基于密度的空间聚类(DBSCAN)划分与预测划分相结合的联合划分算法对量测集进行划分

然后采用联合概率数据关联(JPDA)的软关联思想建立量测簇与扩展目标之间的对应关系

最后采用随机矩阵法对扩展目标进行估计获得运动状态和形状信息

特点是:将量测集划分为互不相交的几个簇

以使每个簇中的量测源于同一目标或杂波; 建立量测簇与扩展目标之间的关联关系及状态更新。联合划分算法与DBSCAN划分的比较仿真实验表明

在有距离相近目标时采用联合划分算法比采用DBSCAN划分的滤波器的跟踪效果好得多。所提多扩展目标滤波器与ET-GMPDH滤波器的仿真实验表明

所提算法有较高的跟踪精度、较大的检测概率及较小的虚警概率。

Abstract

A multiple extended target filter based on random matrix is proposed to track the kinematic states and shape information of multiple targets in the presence of the clutter measurements. The proposed filter employs a joint partitioning algorithm

which combines the DBSCAN(density based spatial clustering of applications with noise)and the prediction partitioning algorithm

to partition the measurement set. Then

the JPDA(joint probabilistic data association)

which is a soft association algorithm

is applied to description of the association between measurement clusters and extended targets. Finally

the method of random matrix is employed to estimate the kinematic states and shape information of extended targets. Simulation results which compare the joint partitioning algorithm with DBSCAN partitioning show that the filter by using joint partitioning algorithm could achieve much better tracking performance than that by using DBSCAN partitioning when there are spatially close extended targets. Moreover

simulation results in comparison with the ET-GMPHD filter show that the proposed multiple extended target filter has higher tracking accuracy

higher detection probability

and lower false alarm probability.

关键词

Keywords

references

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