"Joint Design of Radar Waveform and Detector via End-to-End Learning wi" by Wei Jiang, Alexander M. Haimovich et al.
 

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

Document Type

Article

Publication Date

2-1-2022

Abstract

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this article. 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 the detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models, it is shown that radar learning can be robustified to uncertainties about environment 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.

Identifier

85124833167 (Scopus)

Publication Title

IEEE Transactions on Aerospace and Electronic Systems

External Full Text Location

https://doi.org/10.1109/TAES.2021.3103560

e-ISSN

15579603

ISSN

00189251

First Page

552

Last Page

567

Issue

1

Volume

58

Grant

725731

Fund Ref

Horizon 2020 Framework Programme

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