基于最小模糊误差熵的机动目标跟踪新方法

陈咏茵; 刘全仲; 李良群; 康莉

doi:10.12263/DZXB.20211693

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基于最小模糊误差熵的机动目标跟踪新方法

学术论文 | 更新时间：2025-12-08

- 基于最小模糊误差熵的机动目标跟踪新方法
- A New Method of Maneuvering Target Tracking Based on Minimum Fuzzy Error Entropy
- 电子学报 2023年51卷第9期页码：2408-2418
- 作者机构：
  
  1.深圳大学电子与信息工程学院,广东深圳 518060
  2.深圳大学广东省智能信息处理重点实验室,广东深圳 518060
  3.中国长城科技集团有限公司,广东深圳 518057
- 作者简介：
  
  陈咏茵女，1997年9月出生于广东省深圳市.现为深圳大学硕士研究生，从事目标跟踪方面的研究工作.E-mail: chenyya0917@163.com
  刘全仲男，教授级高工，2006年毕业于国防科技大学西安通信学院信息工程专业. 主要研究方向为“PKS”技术体系的自主安全计算机技术.E-mail: liuquanzhong@greatwall.com.cn
  李良群男，1979年出生于湖南省邵阳市， 2007年毕业于西安电子科技大学，获博士学位.现为深圳大学信息工程学院教师，主要研究方向为多传感器信息融合、多目标跟踪.E-mail: lqli@szu.edu.cn
- 基金信息：
  
  国家自然科学基金(62171287;81960312)
- DOI：10.12263/DZXB.20211693
  中图分类号： TN820.4
- 收稿：2021-12-23，
  
  修回：2022-09-16，
  
  纸质出版：2023-09-25
- 稿件说明：
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陈咏茵,刘全仲,李良群等.基于最小模糊误差熵的机动目标跟踪新方法[J].电子学报,2023,51(09):2408-2418.

CHEN Yong-yin,LIU Quan-zhong,LI Liang-qun,et al.A New Method of Maneuvering Target Tracking Based on Minimum Fuzzy Error Entropy[J].ACTA ELECTRONICA SINICA,2023,51(09):2408-2418.
陈咏茵,刘全仲,李良群等.基于最小模糊误差熵的机动目标跟踪新方法[J].电子学报,2023,51(09):2408-2418. DOI： 10.12263/DZXB.20211693.

CHEN Yong-yin,LIU Quan-zhong,LI Liang-qun,et al.A New Method of Maneuvering Target Tracking Based on Minimum Fuzzy Error Entropy[J].ACTA ELECTRONICA SINICA,2023,51(09):2408-2418. DOI： 10.12263/DZXB.20211693.

摘要

为了提高不同噪声影响下机动目标跟踪的性能，提出了一种基于最小模糊误差熵无迹滤波（Minimum Fuzzy Error Entropy Unscented Filter， MFEE-UF）机动目标跟踪新方法.在提出方法中，通过引入模糊隶属度表示不同误差样本对估计结果的不同影响，构建最小模糊误差熵准则（Minimum Fuzzy Error Entropy Criterion， MFEEC），解决了普通误差熵中的权重单一化问题，并利用该准则优化无迹滤波；在推导MFEE-UF过程中，首先利用无迹变换（Unscented Transformation， UT）框架得到先验状态估计和先验协方差估计，并通过系统重建得到误差信息，再根据MFEEC构建目标函数，最后利用定点迭代法递归求得后验状态估计结果和后验协方差估计.此外，本文采用一种自适应的核宽设置方法.实验结果表明，该算法能够具有良好的目标跟踪效果，且表现出较强的稳定性.

Abstract

In order to improve the accuracy of target tracking results in nonlinear systems under different kinds of noise

minimum fuzzy error entropy unscented filter (MFEE-UF) is proposed in this paper. In this proposed method

the fuzzy membership is introduced to represent the different effects of different error samples on the estimation results

solving the problem of same weight in common error entropy. And then the minimum fuzzy error entropy criterion (MFEEC) is constructed and used to optimize the unscented filtering

deriving MFEE-UF. In this proposed algorithm

the unscented transformation (UT) framework is used to obtain a priori state estimation and a priori covariance estimation

and error information is obtained by system reconstruction. Then the objective function is constructed based on MFEEC

and finally the posterior state estimation and the posterior covariance estimation is solved by using fixed-point iteration method. In addition

kernel width is set adaptively. Simulations show that the proposed algorithm has strong stability

and can track a target more accurately.

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Keywords

references

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