Radar Waveform Synthesis Using Generative Adversarial Networks

Vesa Saarinen, Visa Koivunen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)


In this paper we propose a machine learning approach based on generative adversarial networks (GAN) for synthesizing novel radar waveforms with a desirable Ambiguity Function (AF) shape and constant modulus property. There are only a limited number of code sequences of a certain length in many widely used radar code families, which may be a drawback in modern radar applications. Hence, there is a need to generate new waveforms for future MIMO, multifunction, and cognitive radars. In such systems multiple waveforms are launched simultaneously in order to deal with low observable targets or a large number of small targets. Additionally, the ability to generate new waveforms at will makes it more difficult for an adversary to recognize or detect that it is illuminated by a radar. A Wasserstein GAN (WGAN) structure is developed for complex-valued input data. The model is trained using Frank and Oppermann codes with good autocorrelation and crosscorrelation properties. The synthesized novel waveforms have an almost identical AFs to those of the training data, as well as a low cross-correlation relative to the codes in the training set. Additionally, the constant modulus property facilitates the efficient use of amplifiers.

Original languageEnglish
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
Number of pages6
ISBN (Electronic)9781728189420
Publication statusPublished - 21 Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Radar Conference - Florence, Italy
Duration: 21 Sep 202025 Sep 2020

Publication series

NameIEEE Radar Conference
ISSN (Print)1097-5659
ISSN (Electronic)2375-5318


ConferenceIEEE Radar Conference
Abbreviated titleRadarCon


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