Probability Hypothesis Density Filter-Based Group Target Tracking Algorithm Using Rigid-Body Similarity Model and Measurement Fusion: Implementations Across Random Finite Set Frameworks

IF 1.5 4区 管理学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Kai Chang, Haitao Wang, Tian Xia, Li Wang, Ziqiang Chen
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引用次数: 0

Abstract

This paper addresses the measurement quality optimisation problem in multiple resolvable group target tracking (MRGTT), proposing an improved MRGTT algorithm based on rigid-body similarity model and optimal measurement fusion. Firstly, a unified framework for group target motion and measurement description is established by introducing the rigid-body similarity model. Secondly, an optimal measurement fusion scheme derived from the minimum variance criterion is proposed, which achieves 2.5–2.8 times faster convergence speed compared to traditional equal-weight methods. Furthermore, a complete algorithm flowchart integrating group structure construction, measurement optimisation and intensity update is designed. The proposed method demonstrated exceptional adaptability across different random finite set (RFS) filtering frameworks, including Gaussian mixture probability hypothesis density (GM-PHD) and Poisson multi-Bernoulli mixture (PMBM). Simulation results show that the proposed method achieves significant improvements in OSPA distance over traditional algorithms, with 45% improvement in the GM-PHD implementation and robust performance across diverse scenario complexities in the PMBM framework, including large-scale manoeuvring scenarios. This framework-agnostic approach provides a versatile solution for resolvable group target tracking in complex scenarios such as group splitting, merging and high-clutter environments.

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基于概率假设密度滤波的刚体相似模型和测量融合群目标跟踪算法:跨随机有限集框架的实现
针对多可解群目标跟踪(MRGTT)中的测量质量优化问题,提出了一种基于刚体相似模型和最优测量融合的MRGTT改进算法。首先,通过引入刚体相似度模型,建立了群体目标运动和测量描述的统一框架;其次,提出了一种基于最小方差准则的最优测量融合方案,其收敛速度比传统等权方法快2.5 ~ 2.8倍;设计了集群结构构建、测量优化和强度更新于一体的完整算法流程图。该方法在高斯混合概率假设密度(GM-PHD)和泊松-伯努利混合(PMBM)等不同的随机有限集(RFS)滤波框架下具有良好的适应性。仿真结果表明,与传统算法相比,该方法在OSPA距离上取得了显著的改进,GM-PHD实现的性能提高了45%,并且在PMBM框架中包括大规模机动场景在内的各种场景复杂性下具有鲁棒性。这种与框架无关的方法为在群体分裂、合并和高杂波环境等复杂场景下的可分辨群体目标跟踪提供了一种通用的解决方案。
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来源期刊
Iet Radar Sonar and Navigation
Iet Radar Sonar and Navigation 工程技术-电信学
CiteScore
4.10
自引率
11.80%
发文量
137
审稿时长
3.4 months
期刊介绍: IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications. Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.
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