Optimal Policies Search for Sensor Management

TL;DR

This paper presents a novel method for sensor management that learns optimal policies offline using stochastic gradient estimation and applies it to radar systems, demonstrating promising simulation results.

Contribution

It introduces a new stochastic gradient-based approach for deriving optimal sensor management policies using IPA, applicable in simulation-based offline learning.

Findings

Effective policy learning in radar management demonstrated

New gradient approximation method based on IPA introduced

Simulation results show promising performance

Abstract

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.