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

Gyuyeol Kong, Minchae Jung, Visa Koivunen

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

6 Citations (Scopus)
141 Downloads (Pure)


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 publication2020 IEEE International Radar Conference, RADAR 2020
Number of pages6
ISBN (Electronic)9781728168128
Publication statusPublished - Apr 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Radar Conference - Washington, United States
Duration: 28 Apr 202030 Apr 2020


ConferenceIEEE Radar Conference
Abbreviated titleRADAR
Country/TerritoryUnited States


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