Results 11 to 20 of about 355 (164)
A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms
Remote SensingThe estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability
Qi Jiang +4 more
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Tracking Ground Targets with a Road Constraint Using a GMPHD Filter [PDF]
Sensors, 2018 The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its ...
Jihong Zheng, Meiguo Gao
doaj +3 more sources
Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter [PDF]
Sensors, 2016 The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets.
Weijian Si, Zhiyu Qu
exaly +3 more sources
An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation
IEEE Journal on Selected Topics in Signal Processing, 2013 This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently.
Christian Lundquist +2 more
exaly +6 more sources
Trajectory PHD and CPHD Filters [PDF]
IEEE Transactions on Signal Processing, 2019 This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles.
Ángel F. García-Fernández +1 more
core +7 more sources
Active Sonar Target Tracking Based on the GM-CPHD Filter Algorithm [PDF]
Xibei Gongye Daxue Xuebao, 2018 The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system ...
doaj +2 more sources
Cluster Target Tracking Based on Multi-Sensor Adaptive GLMB Filter [PDF]
SensorsIn complex detection environments, unknown detection probability and clutter rate hinder accurate tracking of cluster targets. To address this issue, this paper proposes a novel multi-sensor adaptive generalized labeled multi-Bernoulli (MS-AGLMB) filter.
Zheng Zhang, Daozhi Wei, Xirui Xue
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A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking [PDF]
SensorsMost multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of ...
Liu Wang +4 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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Continuous-Discrete Multiple Target Filtering: PMBM, PHD and CPHD Filter Implementations [PDF]
IEEE Transactions on Signal Processing, 2020 This article develops models and algorithms for continuous-discrete multiple target filtering, in which the multi-target system is modelled in continuous time and measurements are available at discrete time steps. In order to do so, this paper first proposes a statistical model for multi-target appearance, dynamics and disappearance in continuous time,
Ángel F. García-Fernández +1 more
openaire +3 more sources