Ambiguity Function Based Radar Waveform Classification and Unsupervised Adaptation Using Deep CNN Models

Pavel Itkin, Nadav Levanon

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

Abstract

We present a robust generalized approach to phase and frequency modulated LPI Radar waveform classification and adaptation, inspired by deep convolutional neural architectures. We use a complex Ambiguity Function matrix as a pre-processing step, following which, a waveform classification, or adaptation to unlabeled reference target domains, is performed. We test our method on a wide range of tasks, datasets, and different signal distributions. Our method surpasses the state-of-the-art performance on classification problems on multi-encoding, multi-feature datasets, in diverse and challenging conditions. Our novel approach to an unlabeled Radar waveform adaptation reveals impressive classification improvements to domain shifted unlabeled signals.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538695494
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2019 - Tel-Aviv, Israel
Duration: 4 Nov 20196 Nov 2019

Publication series

Name2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2019

Conference

Conference2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2019
Country/TerritoryIsrael
CityTel-Aviv
Period4/11/196/11/19

Keywords

  • Ambiguity function
  • EW
  • LPI
  • convolutional neural networks
  • domain adaptation
  • waveform classification

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