Track-Before-Detect algorithm based on cardinalized probability hypothesis density filter
نویسندگان
چکیده
منابع مشابه
Classification Aided Cardinalized Probability Hypothesis Density Filter
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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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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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...
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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...
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ژورنال
عنوان ژورنال: Journal of Infrared and Millimeter Waves
سال: 2013
ISSN: 1001-9014
DOI: 10.3724/sp.j.1010.2013.00437