Results 21 to 30 of about 40,312 (249)
Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter
IEEE Access, 2019 Compared with the single sensor tracking system, the multi-sensor tracking system has several advantages in target tracking, such as a larger field of view and higher tracking accuracy.
Long Liu +3 more
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Measurement + Control, 2021 Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number.
Weijun Xu
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Improving visual multi‐object tracking algorithm via integrating GM‐PHD and correlation filter
IET Image Processing, 2022 The traditional visual multi‐object tracking methods based on the Gaussian mixture probability hypothesis density filter are generally not well adapted for tracking the targets in the complex scenarios, where there are a large number of unknowable ...
Jinlong Yang +3 more
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IET Radar, Sonar & Navigation, 2022 Non‐cooperative bistatic radar refers to the passive bistatic radar using a non‐cooperative radar as the illuminator of opportunity. Limited by the non‐cooperation and bistatic configuration, multi‐target tracking of the non‐cooperative bistatic radar is
Sen Wang, Qinglong Bao, Jiameng Pan
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Multisensor Vehicle Tracking with the Probability Hypothesis Density Filter [PDF]
2006 9th International Conference on Information Fusion, 2006 In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system.
Mirko Mählisch +3 more
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State Estimation and Smoothing for the Probability Hypothesis Density Filter [PDF]
, 2021 <p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements.
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Improved Bearings-Only Multi-Target Tracking with GM-PHD Filtering
Sensors, 2016 In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking.
Qian Zhang, Taek Lyul Song
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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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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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Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter [PDF]
Radioengineering, 2016 This paper develops a box-particle implementation of cardinalized probability hypothesis density filter to track multiple targets and estimate the unknown number of targets.
L. Song, M. Liang, H. Ji
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