Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising

Abstract

Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functions for denoising, with a particular focus on non-stationary signal processing in practical applications}. We integrate Wiener filter principle and the time-frequency filtering mechanism of CCTFD to design the least-squares adaptive filter method in the Wigner-Ville distribution (WVD) domain, giving birth to the least-squares adaptive filter-based CCTFD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error denoising in the WVD domain. {Numerical experiments on typical simulated radar signals and real-world electrocardiogram data comprehensively demonstrate that the proposed adaptive CCTFD outperforms several state-of-the-art methods in noise suppression.