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Free clustering optimal particle probability hypothesis density (PHD) filter
Journal of Central South University, 2014As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density (P-PHD) filter would decline when clustering algorithm is used to extract target states, a free clustering optimal P-PHD (FCO-P-PHD) filter is proposed. This method can lead to obtainment
Yun-xiang Li +4 more
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Adaptive target birth intensity for Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter
2014 IEEE International Conference on Control Science and Systems Engineering, 2014In standard formulation of Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, the newborn target intensity function is regarded as a known prior probability. This assumption limited the application in practice. An improved method is proposed based on the standard GMPHD by introducing logicals to differentiate two types of targets, called ...
Yan Cang, Di Chen, Weijin Sun
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Improved Probability Hypothesis Density (PHD) Filter for Multitarget Tracking
2005 3rd International Conference on Intelligent Sensing and Information Processing, 2005The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the PHD function, the first order moment of the posterior multi-target density, from which the number of targets as well as their individual states can be extracted.
K. Panta, B. Vo, S. Singh
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On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter
SPIE Proceedings, 2011This paper considers the effect of sensor ordering on the iterated-corrector PHD update. It is known that changing the order of the updates results in different PHDs, however, these are usually not significantly different. This paper considers a multisensor scenario using a single poor quality sensor in combination with good sensors, where the bad ...
Sharad Nagappa, Daniel E. Clark
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2007 Information, Decision and Control, 2007
The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the posterior intensity (or a first-order moment) of the random sets of targets, from which the number as well as individual states can be estimated.
Kusha Panta, Ba-Ngu Vo
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The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the posterior intensity (or a first-order moment) of the random sets of targets, from which the number as well as individual states can be estimated.
Kusha Panta, Ba-Ngu Vo
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Forward-Backward Probability Hypothesis Density Smoothing
IEEE Transactions on Aerospace and Electronic Systems, 2012 A forward-backward probability hypothesis density (PHD) smoother involving forward filtering followed by backward smoothing is proposed. The forward filtering is performed by Mahler's PHD recursion.
Ba Tuong Võ +2 more
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A Multiple-Detection Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing, 2015 Most conventional target tracking algorithms assume that one target can generate at most one detection per scan. However, in many practical target tracking applications, one target may generate multiple detections in one scan, because of multipath ...
Xu Tang +2 more
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2018 AIAA Information Systems-AIAA Infotech @ Aerospace, 2018
Peng Mun Siew +2 more
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Peng Mun Siew +2 more
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