Open AccessJournal Article
Improved Multiple Model Particle PHD and CPHD Filters
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TLDR
In this paper, an improved multiple model probability hypothesis density (PHD) filter is proposed to solve the particle degenerate problem and the Poisson assumption for the target number distribution.About:
This article is published in Acta Automatica Sinica.The article was published on 2012-01-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Conditional probability.read more
Citations
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Journal ArticleDOI
Adaptive genetic MM-CPHD filter for multitarget tracking
Bo Li,Jianli Zhao,Fu-Wen Pang +2 more
TL;DR: The adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles and the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets.
Proceedings ArticleDOI
Particle filter-based algorithm for multiple maneuvering targets tracking
Zhang Mao-lin,Wu Yunjie +1 more
TL;DR: In this paper, a tracking problem that multiple targets waiting to be tracked accurately is solved by using particle filter to solve nonlinear and non Gaussian and multiple model filtering to track maneuvering targets.
Proceedings ArticleDOI
On adaptive probability hypothesis density filter for multi-target tracking
TL;DR: In this paper, an adaptive probability hypothesis density (PHD) filter was proposed for multi-target tracking, where both the completed clutter process and the single-target measurement likelihood were improved based on the Bayesian theory.
Journal ArticleDOI
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion
TL;DR: The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.
Journal ArticleDOI
Improved Multi-target Tracking Algorithm Based on Gaussian Mixture Particle PHD Filter
TL;DR: Gaussian mixture particle probability hypothesis density filter (PHD) algorithm is proposed, which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter.
References
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Journal ArticleDOI
Adaptive genetic MM-CPHD filter for multitarget tracking
Bo Li,Jianli Zhao,Fu-Wen Pang +2 more
TL;DR: The adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles and the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets.
Proceedings ArticleDOI
Particle filter-based algorithm for multiple maneuvering targets tracking
Zhang Mao-lin,Wu Yunjie +1 more
TL;DR: In this paper, a tracking problem that multiple targets waiting to be tracked accurately is solved by using particle filter to solve nonlinear and non Gaussian and multiple model filtering to track maneuvering targets.
Proceedings ArticleDOI
On adaptive probability hypothesis density filter for multi-target tracking
TL;DR: In this paper, an adaptive probability hypothesis density (PHD) filter was proposed for multi-target tracking, where both the completed clutter process and the single-target measurement likelihood were improved based on the Bayesian theory.
Journal ArticleDOI
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion
TL;DR: The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.
Journal ArticleDOI
Improved Multi-target Tracking Algorithm Based on Gaussian Mixture Particle PHD Filter
TL;DR: Gaussian mixture particle probability hypothesis density filter (PHD) algorithm is proposed, which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter.