End-to-end Learning of Waveform Generation and Detection for Radar Systems

TL;DR

This paper introduces an end-to-end neural network-based approach for jointly designing radar waveforms and detectors, improving robustness and adaptability in complex environments without prior target or clutter models.

Contribution

It presents a novel alternating training method for jointly optimizing radar waveform and detector using supervised and reinforcement learning, without relying on prior environmental knowledge.

Findings

Achieves more robust radar detection in cluttered and noisy environments.

Successfully adapts transmitted waveforms to environmental conditions.

Outperforms conventional radar methods in numerical simulations.

Abstract

An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately: For a fixed transmitted waveform, the detector is trained using supervised learning so as to approximate the Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is trained using reinforcement learning based on feedback from the receiver. No prior knowledge is assumed about the target and clutter models. Both transmitter and receiver are implemented as feedforward neural networks. Numerical results show that the proposed end-to-end learning approach is able to obtain a more robust radar performance in clutter and colored noise of arbitrary probability density functions as compared to conventional methods, and to successfully adapt the transmitted waveform to environmental conditions.