基于高效标签匹配的分布式标签多伯努利多目标跟踪方法

丁昌文; 邵春涛; 周斯腾; 周荻; 杜润乐; 刘佳琪

doi:10.1631/FITEE.2400582

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基于高效标签匹配的分布式标签多伯努利多目标跟踪方法

常规文章 | Updated：2025-04-03

- 基于高效标签匹配的分布式标签多伯努利多目标跟踪方法
  Enhanced Publication
- Distributed multi-target tracking with labeled multi-Bernoulli filter considering efficient label matching
- 信息与电子工程前沿（英文） 2025年26卷第3期页码：400-414
- Affiliations：
  
  1.School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
  2.National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing 100076, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2400582
  中图分类号： TP391
- 收稿：2024-07-11，
  
  修回：2024-10-25，
  
  网络出版：2025-03-13，
  
  纸质出版：2025-03
- Accepted：
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丁昌文, 邵春涛, 周斯腾, 等. 基于高效标签匹配的分布式标签多伯努利多目标跟踪方法[J]. 信息与电子工程前沿（英文）, 2025,26(3):400-414.

Changwen DING, Chuntao SHAO, Siteng ZHOU, et al. Distributed multi-target tracking with labeled multi-Bernoulli filter considering efficient label matching[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(3): 400-414.
丁昌文, 邵春涛, 周斯腾, 等. 基于高效标签匹配的分布式标签多伯努利多目标跟踪方法[J]. 信息与电子工程前沿（英文）, 2025,26(3):400-414. DOI： 10.1631/FITEE.2400582.

Changwen DING, Chuntao SHAO, Siteng ZHOU, et al. Distributed multi-target tracking with labeled multi-Bernoulli filter considering efficient label matching[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(3): 400-414. DOI： 10.1631/FITEE.2400582.

摘要

本文提出一种基于高效标签匹配的分布式标签多伯努利多目标跟踪方法。传统的分布式标签多伯努利融合都是假设本地标签多目标密度之间的标签匹配已经完成。然而，考虑到实际场景中本地标签多目标密度之间的标签空间相互独立，因此上述假设在很多应用场景中无法保证。为解决上述问题，本文从算术均值散度的概念出发，提出一种高效的标签匹配方法，并根据匹配结果，进行标签多伯努利后验概率密度融合。本文所提方法与已有方法相比，在低检测概率场景中体现出良好性能。此外，为保证融合结果的一致性与完整性，整个融合过程被设计为以下4个阶段：预融合、标签确认、后验概率密度补充和唯一性检查。在具有挑战性的非线性纯方位多目标跟踪（MTT）场景中，验证了所提标签匹配分布式标签多伯努利滤波器融合的性能。

Abstract

We propose a distributed labeled multi-Bernoulli (LMB) filter based on an efficient label matching method. Conventional distributed LMB filter fusion has the premise that the labels among local densities have already been matched. However

considering that the label space of each local posterior is independent

such a premise is not practical in many applications. To achieve distributed fusion practically

we propose an efficient label matching method derived from the divergence of arithmetic average (AA) mechanism

and subsequently label-wise LMB filter fusion is performed according to the matching results. Compared with existing label matching methods

this proposed method shows higher performance

especially in low detection probability scenarios. Moreover

to guarantee the consistency and completeness of the fusion outcome

the overall fusion procedure is designed into the following four stages: pre-fusion

label determination

posterior complement

and uniqueness check. The performance of the proposed label matching distributed LMB filter fusion is demonstrated in a challenging nonlinear bearings-only multi-target tracking (MTT) scenario.

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references

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