Time Budget Management in Multifunction Radars Using Reinforcement Learning

Abstract

An adaptive revisit interval selection (RIS) in multifunction radars is an integral part of efficient time budget management (TBM). In this paper, the RIS problem is formulated as a Markov decision process (MDP) with unknown state transition probabilities and reward distributions. A reward function is proposed to minimize the tracking load (TL) while maintaining the track loss probability (TLP) at a tolerable level. The reinforcement learning (RL) problem is solved using the Qlearning algorithm with an epsilon-greedy policy. Compared to a baseline algorithm, the RL approach was capable of maintaining the tracks while reducing the tracking load significantly.

Artificial intelligence meets radar resource management: A comprehensive background and literature review

“…Another recent work is [114] focussing on adaptive revisit interval selection (RIS) in MFRs as a time management problem formulated as an MDP with unknown state transition probabilities and reward distributions. The reward function is proposed to minimise tracking load|transfer learning (TL) while keeping the track loss probability as an optimisation constraint.…”