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Multiple Model Adaptive Estimator Target Tracker for Maneuvering Targets in Clutter

Multiple Model Adaptive Estimator Target Tracker for Maneuvering Targets in Clutter

Brian D. Smith

26,18 €
IVA incluido
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Editorial:
BiblioScholar
Año de edición:
2012
Materia
Ingeniería: general
ISBN:
9781288369249
26,18 €
IVA incluido
Disponible

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In recent years, the Multiple Hypothesis Tracker has gained acceptance as means of handling targets in ameasurement-clutter environment. MHT algorithms rely on Gaussian mixture representations of a target’s current stateestimate, and the number of components within these mixtures grows exponentially with each successive sensor scan.Previous research into techniques that limit the growth of Gaussian mixture components proved that the Integral SquareError cost-function-based algorithm performs well in this role. Also, multiple-model adaptive algorithms have been shownto handle poorly-known target dynamics or targets that exhibit a large range of maneuverability over time with excellentresults.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

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