Radar Waveform Recognition using Fourier-Based Synchrosqueezing Transform and CNN

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Abstract

In this paper the problem of recognizing radar waveforms is addressed. Waveform classification is needed in spectrum sharing and radar-communications coexistence, cognitive radars 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 processed with Fourier synchrosqueezing transform that has excellent properties in revealing timevarying behavior, rate of, strength and number of oscillatory components in received 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 the polyphase waveforms even at low signal-to-noise ratio regime.
Original languageEnglish
Title of host publication2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
PublisherIEEE
Pages664-668
ISBN (Electronic)9781728155494
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Guadeloupe, Le Gosier, Guadeloupe
Duration: 15 Dec 201918 Dec 2019
https://camsap19.ig.umons.ac.be

Workshop

WorkshopIEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
CountryGuadeloupe
CityLe Gosier
Period15/12/201918/12/2019
Internet address

Keywords

  • Choi-Williams distribution
  • convolutional neural network
  • Fourier-based synchrosqueezing transform
  • radar waveform recognition

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