Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking
نویسندگان
چکیده
منابع مشابه
Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters
The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...
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Target 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 modify the update equations of the recently deri...
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This technical report presents a cardinalized probability hypothesis density (CPHD) lter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) lter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD...
متن کاملAcoustic Channel Tracking with the Cardinalized Probability Hypothesis Density Filter and the Multiple Hypothesis Tracker
Two datasets, one simplistic that assumes direct observation of paths and the other based on observations derived from compressed sensing and an assumed OFDM communications underpinning, simulate underwater acoustic channels. The Cardinalized Probability Hypothesis Density filter and the Multiple Hypothesis Tracker are applied to these wireless channels. The performances of the two trackers are...
متن کاملMultiple Target Tracking with The Probability Hypothesis Density Filter
The random-set framework for multiple target tracking offers a distinct alternative to the traditional approach to multiple target tracking by treating the collections of individual targets and observations as finite-sets. The multi-target state is predicted and updated recursively based on the set-valued observation. The complexity of computing the multi-target recursion grows exponentially wi...
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ژورنال
عنوان ژورنال: Measurement and Control
سال: 2019
ISSN: 0020-2940
DOI: 10.1177/0020294019877494