Results 21 to 30 of about 79,627 (207)
IET Radar, Sonar & Navigation, 2022 The Gaussian mixture probability density (GM‐PHD) filter has become a popular approach to solve the multiple‐target tracking (MTT) problem because it can effectively and efficiently estimate the number of targets and target states that change over time ...
Yi‐Chieh Sun +2 more
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IEEE Access, 2020 The existing Probability Hypothesis Density (PHD) filters with birth intensity estimation only operate on single or two consecutive scan data for multi-target tracking.
Qian Zhu +3 more
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Multiple Object Tracking Based on Background Subtraction Detection and Improved GM-PHD Filter [PDF]
Jisuanji gongcheng, 2017 Target label confusion and loss are usually caused by occlusion and detection missing in multiple object tracking process,which leads to failing tracking.Aiming at this problem,an improved tracking method based on Gaussian Mixture Probability Hypothesis ...
CHEN Xiangqian,MA Shaohui,XU Wenbo
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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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A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers
Sensors, 2018 In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely.
Zhuowei Liu +4 more
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Leida xuebao, 2019 Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives.
PEI Jiazheng +4 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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Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio [PDF]
Radioengineering, 2023 Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS)
S. Xiao +4 more
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Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
Algorithms, 2019 Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC)
Jiangyi Liu +3 more
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Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
IET Signal Processing, 2021 The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets.
Jinlong Yang, Jiuliu Tao, Yuan Zhang
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