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Design and Performance Validation of CWT-MCA Based Interference Mitigation for Automotive Radars

Schnelle Fakten

  • Interne Autorenschaft

  • Weitere Publizierende

    Shengheng Liu, Zheng Zhang, Zhihan Gong, Lian Kou, Danfeng Shan, Lei Li, Yongming Huang

  • Veröffentlichung

    • 2024
  • Zeitschrift/Zeitung

    Digital Signal Processing

  • Organisationseinheit

  • Fachgebiete

    • Kommunikations- und Informationstechnik
  • Format

    Journalartikel (Artikel)

Zitat

S. Liu, Z. Zhang, T. Fei, Z. Gong, L. Kou, D. Shan, L. Li, and Y. Huang, “Design and Performance Validation of CWT-MCA Based Interference Mitigation for Automotive Radars,” Digital Signal Processing, pp. 104644–104657, 2024.

Abstract

Interference mitigation has been a vital area of focus within the realm of automotive radar research, holding paramount importance for augmenting advanced driver-assistance capabilities, particularly in vehicle-dense scenarios. This paper presents a novel interference mitigation scheme based on continuous wavelet transform and morphological component analysis (CWT-MCA). This method, in essence, transforms the time-domain signal into the time-frequency domain, subsequently enabling the direct separation of target and interference components, thereby circumventing the necessity for interference detection and identification. Additionally, this paper introduces a series of MCA algorithms grounded in various transform domains, which are subsequently compared with the traditional linear prediction based approach. We show through comprehensive numerical simulations and real-data experiments that CWT-MCA algorithm exhibits markedly superior interference mitigation performance while offering lower computational complexity, which makes it well-suited for real-time implementation in automotive radar systems.

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