2009/03/19 by Thomas Bréhard, Emmanuel Duflos, Bréhard, Thomas +5
Computer Science · Decision Sciences · Mathematics · #Applications (stat.AP) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Simulation Techniques and Applications #Target Tracking and Data Fusion in Sensor Networks #cs.LG #stat.AP
paper · pdf · doi:10.48550/arxiv.0903.3329
arxiv created 2009/03/19 · openalex publication_date 2009/03/19 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
This paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line (in simulation) using models of the environement and of the sensor(s). The learned policy can then be used to manage the sensor(s). In order to approximate the gradient in a stochastic context, we introduce a new method to approximate the gradient, based on Infinitesimal Perturbation Approximation (IPA). The effectiveness of this general framework is illustrated by the managing of an Electronically Scanned Array Radar. First simulations results are finally proposed.