@Article{cmc.2025.067764,
AUTHOR = {Shih-Lin Lin},
TITLE = {Advanced Multi-Channel Echo Separation Techniques for High-Interference Automotive Radars},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {1},
PAGES = {1365--1382},
URL = {http://www.techscience.com/cmc/v85n1/63572},
ISSN = {1546-2226},
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 m<sup>2</sup>, 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).},
DOI = {10.32604/cmc.2025.067764}
}