Results 31 to 40 of about 1,121,998 (275)
Remote Sensing, 2022 The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of ...
Chao Zhang +4 more
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Paraunitary oversampled filter bank design for channel coding [PDF]
, 2006 Oversampled filter banks (OSFBs) have been considered for channel coding, since their redundancy can be utilised to permit the detection and correction of channel errors.
McWhirter, J.G. +5 more
core +3 more sources
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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Convergence of the SMC implementation of the PHD filter [PDF]
, 2005 The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem.
Singh, Sumeetpal S. (Sumeetpal Sidhu) +9 more
core +1 more source
Marginalized particle PHD filters for multiple object Bayesian filtering [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2014 The Probability Hypothesis Density (PHD) filter is a recent solution to the multi-target filtering problem. Because the PHD filter is not computable, several implementations have been proposed including the Gaussian Mixture (GM) approximations and Sequential Monte Carlo (SMC) methods.
Yohan Petetin +2 more
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source