Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Qiang GUO; Long TENG; Xinliang WU; Wenming SONG; Dayu HUANG

doi:10.1631/FITEE.2200286

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Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Regular Papers | Updated：2022-12-16

- Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
- 未知辐射源信号特征辅助的广义标签多伯努利滤波器
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 12, Pages: 1871-1880(2022)
- Affiliations：
  
  1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  2.China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China
- Author bio：
  
  †E-mail: guoqiang@hrbeu.edu.cn;
  ‡Corresponding author
  wuxinliang51@163.com;
  hdayady@163.com
- Funds：
- DOI：10.1631/FITEE.2200286
  CLC： TN953
- Received：30 June 2022，
  
  Accepted：10 October 2022，
  
  Published：0 December 2022
- Accepted：
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Qiang GUO, Long TENG, Xinliang WU, et al. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1871-1880.
DOI：

Qiang GUO, Long TENG, Xinliang WU, et al. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1871-1880. DOI： 10.1631/FITEE.2200286.

摘要

提出一种未知辐射源信号特征辅助的广义标签多伯努利滤波器。复杂电磁环境下，辐射源特征通常未知且随时间变化。针对辐射源特征未知的问题，提出一种基于数据场动态聚类的辐射源特征求解方法。针对辐射源特征时变以及对应的概率分布未知的问题，提出一种改进的模糊C-均值算法来计算目标和杂波量测的相关系数，以近似辐射源特征的似然函数。在此基础上，将辐射源特征集成到广义标签多伯努利滤波器中，从而获得新的递归方程。仿真结果表明，提出的方法可以提高对多目标的跟踪性能，尤其在强杂波环境中。

Abstract

A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments

emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem

we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown

an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements

to approximate the EF likelihood function. On this basis

the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets

especially in heavy clutter environments.

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references

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