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

arXiv: https://arxiv.org/abs/1811.02359 · HTML: https://mlsanity.com/paper/1811.02359/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs](https://mlsanity.com/paper/2511.02672/) (2025)

## Abstract (arXiv)

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 textitagent, 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 (PD).