高斯混合扩展目标多伯努利滤波器

张光华 1; 连峰 1; 韩崇昭 1; 2; 姚玲玲 1; 2

doi:10.7652/xjtuxb201410002

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高斯混合扩展目标多伯努利滤波器

更新时间：2025-07-09

- 高斯混合扩展目标多伯努利滤波器
- A Gaussian Mixture Extended-Target Multi-Bernoulli Filter
- 西安交通大学学报 2014年48卷第10期页码：9-14
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 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：
  
  : 2014-02-20。作者简介: 张光华(1988—),男,博士生;连峰(通信作者),男,副教授。基金项目: 国家自然科学基金创新研究群体基金资助项目(61221063,61005026);国家“973计划”资助项目(2013CB329405)
- DOI：10.7652/xjtuxb201410002
  中图分类号： TN929.5
- 网络首发：2014-10-10，
  
  纸质出版：2014
- 稿件说明：
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张光华 1, 连峰 1, 韩崇昭 1, 等. 高斯混合扩展目标多伯努利滤波器[J]. 西安交通大学学报, 2014,48(10):9-14.

A Gaussian Mixture Extended-Target Multi-Bernoulli Filter[J]. 2014, 48(10): 9-14.
张光华 1, 连峰 1, 韩崇昭 1, 等. 高斯混合扩展目标多伯努利滤波器[J]. 西安交通大学学报, 2014,48(10):9-14. DOI： 10.7652/xjtuxb201410002.

A Gaussian Mixture Extended-Target Multi-Bernoulli Filter[J]. 2014, 48(10): 9-14. DOI： 10.7652/xjtuxb201410002.

摘要

针对杂波环境下多扩展目标跟踪中数据关联过程复杂的问题

提出一种可同时估计扩展目标状态和目标数的高斯混合扩展目标多伯努利(GM-ET-MBer)滤波器

该滤波器无需对测量与扩展目标进行关联。首先采用伯努利随机有限集和泊松随机有限集分别描述扩展目标的状态和观测; 然后结合扩展目标状态的预测信息

推导了扩展目标状态的更新方程

并在线性高斯条件下采用高斯混合方法递推地对扩展目标的状态进行估计跟踪。与高斯混合扩展目标概率假设密度(GM-ET-PHD)滤波器相比

GM-ET-MBer滤波器有效地提高了对目标数的估计精度。仿真结果表明

GM-ET-MBer滤波器和GM-ET-PHD滤波器对目标数估计的标准偏差分别为0.267 3和0.395 3

可知所提滤波器对目标数的估计更稳定。

Abstract

A Gaussian mixture extended-target multi-Bernoulli(GM-ET-MBer)filter is proposed to address the complex data association. The filter can simultaneously estimate the state and the number of extended targets without data association between observations and extended targets States and observations of extended targets are modeled as a Bernoulli random finite set and a Poisson random finite set

respectively. An updated state of extended targets is derived by combining the predicted states

and then

the state of extended targets is recursively estimated in linear Gaussian models. Compared with the Gaussian mixture extended-target probability hypothesis density(GM-ET-PHD)filter

the GM-ET-MBer filter can effectively improve estimation accuracy to the number of extended targets. Simulation results show that estimation of the proposed filter to the number of targets is unbiased and the standard deviations of estimations are 0.267 3 and 0.395 3

respectively

for both the GM-ET-MBer filter and GM-ET-PHD filter.

关键词

Keywords

references

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