Results 11 to 20 of about 480,134 (154)
A CPHD Filter for Tracking With Spawning Models [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2013 In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only.
Lennart Svensson, Lars Hammarstrand
exaly +4 more sources
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
doaj +4 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
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 +10 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
doaj +2 more sources
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 +2 more sources
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
doaj +2 more sources
Computationally-Tractable Approximate PHD and CPHD Filters for Superpositional Sensors [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2013 In this paper we derive computationally-tractable approximations of the Probability Hypothesis Density (PHD) and Cardinalized Probability Hypothesis Density (CPHD) filters for superpositional sensors with Gaussian noise. We present implementations of the filters based on auxiliary particle filter approximations.
Santosh Nannuru +2 more
core +4 more sources
High Expression of IGSF10 Confers an Inhibitory Effect on the Progression of Lung Adenocarcinoma. [PDF]
J Cell Mol MedABSTRACT Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer‐related deaths worldwide. Unlike conventional treatments, the targeted therapies or emerging immunotherapies have shown significant advantages in the management of advanced lung cancer.
Cheng L +5 more
europepmc +2 more sources