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Gaussian mixture particle flow probability hypothesis density filter
2017 20th International Conference on Information Fusion (Fusion), 2017The probability hypothesis density (PHD) filter is a promising filter for multi-target tracking which propagates the posterior intensity of the multi-target state. In this paper, a Gaussian mixture particle flow PHD (GMPF-PHD) filter is proposed which uses a bank of particles to represent the Gaussian components in the Gaussian mixture PHD (GM-PHD ...
Mingjie Wang +3 more
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Clustering based box-particle probability hypothesis density filtering
2017 20th International Conference on Information Fusion (Fusion), 2017This paper investigates the box-particle filter for multi-target tracking, and proposes a clustering based box-particle implementation of PHD filter. A subdivision step is added before the estimation of states. Each box is divided into several sub-box based on the estimated number of targets. An equivalent set of particles can be extracted from the set
Wei Li 0087, Chongzhao Han
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Probability hypothesis density filter for multitarget multisensor tracking
2005 7th International Conference on Information Fusion, 2005Multiple target tracking techniques require data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approach (MHT/assignment) may not give satisfactory results, mainly due to the difficulty in deciding the number of targets.
O. Erdinc, P. Willett, Y. Bar-Shalom
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Classification aided cardinalized probability hypothesis density filter
SPIE Proceedings, 2012Target class measurements, if available from automatic target recognition systems, can be incorporated into multiple target tracking algorithms to improve measurement-to-track association accuracy. In this work, the performance of the classifier is modeled as a confusion matrix, whose entries are target class likelihood functions that are used to ...
Ramona Georgescu, Peter Willett
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Multiple model cardinalized probability hypothesis density filter
SPIE Proceedings, 2011The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets.
Ramona Georgescu, Peter Willett
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Data Association for Cardinalized Probability Hypothesis Density Filter
2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), 2009The main drawback of the cardinalized probability hypothesis density (CPHD) filter is that it can't identify the trajectories of different targets. A data association method, the CPHD filter combined with joint probabilistic data association (JPDA), is presented to track multiple targets in dense clutter.
Yang Wang, Zhongliang Jing, Shiqiang Hu
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Gaussian Particle Implementations of Probability Hypothesis Density Filters
2007 IEEE Aerospace Conference, 2007The probability hypothesis density (PHD) filter is a multiple-target filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed.
Daniel Clark, Ba-Tuong Vo, Ba-Ngu Vo
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Gaussian Mixture Probability Hypothesis Density Filter with State-Dependent Probabilities
2021 European Control Conference (ECC), 2021Yi-Chieh Sun, Inseok Hwang 0002
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