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

Li Wang; Stefano Fortunati; Maria Sabrina Greco; Fulvio Gini

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arXiv · 1811.02359

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

Li Wang·Stefano Fortunati·Maria Sabrina Greco·Fulvio Gini

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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 ($P_D$).

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BibTeX RIS

Li Wang, Stefano Fortunati, Maria Sabrina Greco, Fulvio Gini. 2018-11-06. Reinforcement learning-based waveform optimization for MIMO multi-target detection. https://arxiv.org/abs/1811.02359

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