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

Thomas Bréhard 1 Pierre-Arnaud Coquelin 1 Emmanuel Duflos 1, *
* Corresponding author
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).
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Submitted on : Monday, November 19, 2007 - 10:52:43 AM
Last modification on : Thursday, January 20, 2022 - 4:12:31 PM
Long-term archiving on: : Tuesday, September 21, 2010 - 2:38:02 PM


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  • HAL Id : inria-00188292, version 2



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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