A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets [PDF]
Sensors, 2021 In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction
Yiyue Gao +3 more
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A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise [PDF]
Sensors, 2021 In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation.
Yang Gong, Chen Cui
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Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance [PDF]
Sensors, 2021 Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene.
Zhentao Hu +4 more
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Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking [PDF]
Sensors, 2022 Multi-target tracking (MTT) is one of the most important functions of radar systems. Traditional multi-target tracking methods based on data association convert multi-target tracking problems into single-target tracking problems.
Jin Tao +5 more
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Refined PHD Filter for Multi-Target Tracking under Low Detection Probability [PDF]
Sensors, 2019 Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking.
Sen Wang, Qinglong Bao, Zengping Chen
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Label GM-PHD Filter Based on Threshold Separation Clustering [PDF]
Sensors, 2021 Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT).
Kuiwu Wang, Qin Zhang, Xiaolong Hu
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Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter [PDF]
SensorsModern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment.
Christian Bader, Volker Schwieger
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RCS–Doppler-Assisted MM-GM-PHD Filter for Passive Radar in Non-Uniform Clutter [PDF]
SensorsIn passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets.
Jia Wang +3 more
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Extended emitter target tracking using GM-PHD filter. [PDF]
PLoS ONE, 2014 If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis ...
Youqing Zhu +4 more
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Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking
Remote Sensing, 2023 This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets ...
Zhiyuan Yang +4 more
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