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

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 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 $(P_{D})$.