Optimal Policies Search for Sensor Management : Application to the AESA Radar

Thomas Bréhard 1 Pierre-Arnaud Coquelin 1 Emmanuel Duflos 1, *
* Auteur correspondant
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This report introduces a new approach to solve sensor management problems. Classically sensor management problems are formalized as Partially-Observed Markov Decision Process (POMPD). Our original approach consists in deriving the optimal parameterized policy based on stochastic gradient estimation. Two differents techniques nammed Infinitesimal Approximation (IPA) and Likelihood Ratio (LR) can be used to adress such a problem. This report discusses how these methods can be used for gradient estimation in the context of sensor management . The effectiveness of this general framework is illustrated by the managing of an Active Electronically Scanned Array Radar (AESA Radar).
Type de document :
Rapport
[Research Report] RR-6361, INRIA. 2007, pp.21
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https://hal.inria.fr/inria-00188292
Contributeur : Rapport de Recherche Inria <>
Soumis le : lundi 19 novembre 2007 - 10:52:43
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
Document(s) archivé(s) le : mardi 21 septembre 2010 - 14:38:02

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Thomas Bréhard, Pierre-Arnaud Coquelin, Emmanuel Duflos. Optimal Policies Search for Sensor Management : Application to the AESA Radar. [Research Report] RR-6361, INRIA. 2007, pp.21. 〈inria-00188292v2〉

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