Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints

TL;DR

This paper introduces two end-to-end learning approaches for joint radar waveform and detector design, incorporating operational constraints and environmental adaptability, moving beyond traditional model-based methods.

Contribution

It proposes novel deep learning frameworks for radar system design that integrate waveform constraints and environmental robustness, enabling adaptive and data-driven optimization.

Findings

The methods effectively incorporate waveform constraints like PAR and spectral compatibility.

The approaches demonstrate robustness by training with synthetic data from multiple models.

The proposed techniques adapt waveforms to environmental conditions while satisfying design constraints.

Abstract

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this paper. We propose two novel learning-based approaches to waveform and detector design based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models with limited applicability, it is shown that radar learning can be robustified by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints.