Reinforcement learning-based waveform optimization for MIMO multi-target detection

TL;DR

This paper introduces a reinforcement learning-based algorithm for optimizing waveforms in MIMO radar systems to improve multi-target detection by adaptively sensing the environment and tailoring the beam pattern.

Contribution

It presents a novel RL framework for waveform optimization in MIMO radars, enabling adaptive sensing and beamforming based on environmental feedback.

Findings

Enhanced detection probability demonstrated through simulations

Adaptive waveform synthesis improves target detection accuracy

RL-based approach outperforms traditional methods

Abstract

A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the \textit{agent}, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection ($P_D$).