Optimal federated fusion of multiple maneuvering targets based on multi-Bernoulli filters

Yu XUE; Xi'an FENG

doi:10.1631/FITEE.2400598

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Optimal federated fusion of multiple maneuvering targets based on multi-Bernoulli filters

Regular Papers | Updated：2025-06-04

- Optimal federated fusion of multiple maneuvering targets based on multi-Bernoulli filters
- 基于多伯努利滤波器的多机动目标最优联邦融合
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 5, Pages: 753-769(2025)
- Affiliations：
  
  School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Author bio：
  
  E-mail: 18829236362@163.com
  ‡ Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2400598
  CLC： TP273.5
- Received：17 July 2024，
  
  Revised：01 December 2024，
  
  Published Online：02 May 2025，
  
  Published：2025-05
- Accepted：
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Yu XUE, Xi'an FENG. Optimal federated fusion of multiple maneuvering targets based on multi-Bernoulli filters[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 753-769.
DOI：

Yu XUE, Xi'an FENG. Optimal federated fusion of multiple maneuvering targets based on multi-Bernoulli filters[J]. Frontiers of information technology & electronic engineering, 2025, 26(5): 753-769. DOI： 10.1631/FITEE.2400598.

摘要

为实现多个不确定机动目标的最优融合跟踪，提出一种具有分层结构的联合多高斯混合多伯努利（JMGM-MB）滤波器的联邦融合算法。JMGM-MB滤波器以交互多模型（IMM）滤波形式传递每个潜在目标的状态密度，因此精度高于多模型高斯混合多伯努利（MM-GM-MB）滤波器。在分层结构中，每个传感器节点执行局域JMGM-MB滤波器来捕获存活目标、新生目标和消亡目标。所提算法的一个显著特点是在融合节点运行一个主滤波器，以帮助判断状态估计的来源和补充漏检。所有滤波器的输出被关联为多组单目标估计。严格推导了IMM滤波器的最优融合，并将其用于合并关联的单目标估计。引入协方差上界技术以真正消除滤波器间的相关性，进而保证了算法的最优性。仿真结果表明，所提算法在线性和异类场景中均优于现有的集中式和分布式融合算法，且允许灵活调整主滤波器和局域滤波器的相对权重。

Abstract

A federated fusion algorithm of joint multi-Gaussian mixture multi-Bernoulli (JMGM-MB) filters is proposed to achieve optimal fusion tracking of multiple uncertain maneuvering targets in a hierarchical structure. The JMGM-MB filter achieves a higher level of accuracy than the multi-model Gaussian mixture MB (MM-GM-MB) filter by propagating the state density of each potential target in the interactive multi-model (IMM) filtering manner. Within the hierarchical structure

each sensor node performs a local JMGM-MB filter to capture survival

newborn

and vanishing targets. A notable characteristic of our algorithm is a master filter running on the fusion node

which can help identify the origins of state estimates and supplement missed detections. The outputs of all filters are associated into multiple groups of single-target estimates. We rigorously derive the optimal fusion of IMM filters and apply it to merge associated single-target estimates. This optimality is guaranteed by the covariance upper-bounding technique

which can truly eliminate correlations among filters. Simulation results demonstrate that the proposed algorithm outperforms the existing centralized and distributed fusion algorithms in linear and heterogeneous scenarios

and the relative weights of the master and local filters can be adjusted flexibly.

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references

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