Automated tracking of dolphin whistles using Gaussian mixture probability hypothesis density filters [PDF]
The Journal of the Acoustical Society of America, 2016 This work considers automated multi target tracking of odontocete whistle contours. An adaptation of the Gaussian mixture probability hypothesis density (GM-PHD) filter is described and applied to the acoustic recordings from six odontocete species.
Gruden, Pina, White, Paul R.
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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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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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Arbitrary clutter extended target probability hypothesis density filter
IET Radar, Sonar & Navigation, 2021 Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking.
Xinglin Shen +4 more
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A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets
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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Robust adaptive multi‐target tracking with unknown measurement and process noise covariance matrices
IET Radar, Sonar & Navigation, 2022 A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix.
Peng Gu, Zhongliang Jing, Liangbin Wu
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Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters [PDF]
AUT Journal of Electrical Engineering, 2017 The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates ...
M. R. Danaee, F. Behnia
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Multi-Feature Matching GM-PHD Filter for Radar Multi-Target Tracking
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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MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions ...
T. Fuse, D. Hiramatsu, W. Nakanishi
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