Open Access

ARTICLE

Advanced Multi-Channel Echo Separation Techniques for High-Interference Automotive Radars

Shih-Lin Lin^*

Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua, 50007, Taiwan

* Corresponding Author: Shih-Lin Lin. Email:

(This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)

Computers, Materials & Continua 2025, 85(1), 1365-1382. https://doi.org/10.32604/cmc.2025.067764

Received 12 May 2025; Accepted 22 July 2025; Issue published 29 August 2025

Abstract

This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave (FMCW) automotive radar performance under high noise and interference. The four-stage pipeline is applied consecutively: (i) an improved independent component analysis (ICA) blindly separates the two-channel echoes, isolating target and interference components; (ii) a recursive least-squares (RLS) filter compensates amplitude- and phase-mismatches, restoring signal fidelity; (iii) variational mode decomposition (VMD) followed by the Hilbert-Huang Transform (HHT) extracts noise-free intrinsic mode functions (IMFs) and sharpens their time-frequency signatures; and (iv) HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information. Finally, key IMFs are reconstructed into a clean signal, and a beat-frequency estimation via HHT confirms accurate distance results, closely aligning with theoretical predictions. On synthetic data with an input signal-to-noise ratio (SNR) of 12.7 dB, the pipeline delivers a 7.6 dB SNR gain, yields a mean-squared error of 0.25 m², and achieves a range root-mean-square error (Range-RMSE) of 0.50 m. Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature, providing a robust approach for advanced driver assistance systems (ADAS).

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Keywords

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