Results 11 to 20 of about 3,989 (204)

Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

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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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, Shuxin Chen, Hao Wu, Renke He, Lin Hao   +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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Vehicle Detection Based on Probability Hypothesis Density Filter

Sensors, 2016
In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments.
Feihu Zhang, Alois Knoll
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A probability hypothesis density filter for tracking non‐rigid extended targets using spatiotemporal Gaussian process model

IET Signal Processing, 2022
This paper proposes a random finite set (RFS)‐based algorithm to deal with the tracking problem of multiple non‐rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection.
Sunyong Wu, Yusong Zhou, Yun Xie, Qiutiao Xue   +3 more
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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Sensors, 2019
The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between ...
Yulan Han, Chongzhao Han
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A general cardinalized probability hypothesis density filter

EURASIP Journal on Advances in Signal Processing, 2022
Based on random finite set, the probability hypothesis density (PHD) filter and the cardinalized PHD (CPHD) filter have been proposed for multitarget tracking as they are computational tractable.
Xinglin Shen, Zhiyong Song, Hongqi Fan, Qiang Fu   +3 more
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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 ...

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Improved probability hypothesis density filter for multi‐target tracking of non‐cooperative bistatic radar

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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A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors, 2021
In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation.
Yang Gong, Chen Cui
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