Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding

J. Overdevest, A.G.C. Koppelaar, M.J.G. Bekooij, J. Youn, R.J.G. van Sloun

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

1 Citation (Scopus)

Abstract

Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these distortions by utilizing data from one dimension to reconstruct the corrupted samples, which do not perform well when a large number of samples are interfered and have difficulty recovering weak target signals.In this paper, model-based deep learning interference mitigation algorithms, called ALISTA and ALFISTA, are presented that reduce these artifacts by leveraging the full integration gain using all uncorrupted fast-time and slow-time samples. Simulations with 50% corrupted samples show that target peak power loss and velocity peak-to-sidelobe ratio (VPSR) with a 20-layer ALFISTA improves with 5.5 and 9.6 dB compared to zeroing. Furthermore, significant improvements in precision and recall are observed, even when large amounts (50-80%) of samples are missing.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-7281-6327-7
DOIs
Publication statusPublished - 5 May 2023
EventICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Conference

ConferenceICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Keywords

  • ALFISTA
  • ALISTA
  • Deep Unfolding
  • Radar Interference Mitigation
  • Signal Reconstruction

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