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

Document Type

Conference Proceeding

Publication Date

11-1-2019

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.

Identifier

85083305123 (Scopus)

ISBN

[9781728143002]

Publication Title

Conference Record Asilomar Conference on Signals Systems and Computers

External Full Text Location

https://doi.org/10.1109/IEEECONF44664.2019.9049027

ISSN

10586393

First Page

1672

Last Page

1676

Volume

2019-November

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