Waveform Recognition in Multipath Fading Using Autoencoder and CNN with Fourier Synchrosqueezing Transform

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


Research units


In this paper the problem of recognizing radar waveforms is addressed for multipath fading channels. Waveform classification is needed in spectrum sharing, radar-communications coexistence, cognitive radars, spectrum monitoring and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is first equalized to mitigate the effect of multipath fading channels by using a denoising auto-encoder (DAE). Then, the equalized signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing among different radar waveforms even at low signal-to-noise ratio regime.


Original languageEnglish
Title of host publicationProceedings of the IEEE Radar Conference
Publication statusAccepted/In press - 9 Jan 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Radar Conference - Florence, Italy
Duration: 21 Sep 202025 Sep 2020

Publication series

NameProceedings of the IEEE Radar Conference
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318


ConferenceIEEE Radar Conference
Abbreviated titleRadarCon

ID: 40358880