Gaussian Belief Propagation-Based Multiview Multiextended Target Tracking With Occlusion

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-03-13 DOI:10.1109/JSEN.2025.3549141

Yunfei Guo;Hao Zhang;Boting Lin;Hua Su;Yun Chen

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Abstract

To perform multiview (MV) multiextended target tracking (METT) with occlusion, a Gaussian belief propagation (GaBP)-based MV fusion (GaBP-MVF) algorithm is proposed. A concept of “virtual target” is presented to describe the state of an unobstructed part of the target. The “virtual targets” generate the “partial measurements” affected by occlusions through a spatial measurement model. Subsequently, the closed-form joint posterior probability density function (pdf) of virtual targets is formulated. After factorizing the pdf, a factor graph-based GaBP algorithm is derived for moment estimation of virtual targets’ states. Lastly, sensor-derived estimates are regarded as local estimates and forwarded to a fusion center for updating the global estimate. The virtual measurements are generated by a virtual measurement model (VMM) using the predicted global estimate. Then, the global estimate is updated by minimizing the distance between features extracted from virtual measurements and local estimates. The effectiveness of the proposed algorithm is evaluated in simulation and experiment.

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基于高斯信念传播的多视点多扩展目标遮挡跟踪

为了实现具有遮挡的多视点（MV）多扩展目标跟踪（METT），提出了一种基于高斯信念传播（GaBP）的多视点融合（GaBP- mvf）算法。提出了“虚拟目标”的概念来描述目标中无障碍物部分的状态。“虚拟目标”通过空间测量模型生成受遮挡影响的“部分测量”。然后，建立了虚拟目标的封闭式联合后验概率密度函数（pdf）。在对模型进行因子分解后，提出了一种基于因子图的虚拟目标状态矩估计算法。最后，将传感器估计作为局部估计并转发到融合中心更新全局估计。虚拟测量由虚拟测量模型（VMM）根据预测的全局估计值生成。然后，通过最小化从虚拟测量中提取的特征与局部估计之间的距离来更新全局估计。仿真和实验验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice