Jump to content

TRANM: Decoherenced DoA estimation for automotive radar using generalized sparse arrays

Fast facts

  • Internal authorship

  • Further publishers

    Shengheng Liu, Zihuan Mao, Yiran Liu, Markus Gardill, Yongming Huang

  • Publishment

    • 2025
  • Journal

    Signal Processing

  • Organizational unit

  • Subjects

    • Electrical engineering in general
  • Publication format

    Journal article (Article)

Content

This paper tackles the challenge of coherent single-snapshot direction-of-arrival estimation in automotive linear frequency modulated continuous wave (LFMCW) radar using a generalized sparse array. By leveraging atomic-norm minimization (ANM)-based interpolation and Toeplitz rearrangement, a TRANM framework is proposed to address the rank-deficiency issue in the range-Doppler domain. To further enhance computational efficiency, we reformulate the TRANM problem into an equivalent optimization with reduced dimensionality. The problem is then solved using the alternating direction method of multipliers, which provides an optimal solution via an iterative process. Numerical simulations validate that the proposed approach can accurately resolve coherent signals with improved degrees of freedom and achieve super-resolution, all while maintaining a low computational cost.

Notes and references

This site uses cookies to ensure the functionality of the website and to collect statistical data. You can object to the statistical collection via the data protection settings (opt-out).

Settings(Opens in a new tab)