MSSA-based adaptive low-frequency noise reduction using spectrum overlap measure

Abstract

A multistage singular spectrum analysis (MSSA) based method is presented to extract the useful component from the residue stage by stage. In every stage of the decomposition, the sum of the signal-dominated SSA components is treated as the denoised signal. The signal-to-noise ratios (SNRs) of the denoised signals first increase and then decrease. A measurement called spectrum overlap factor (SOF) is proposed to estimate the optimal stage which achieves the highest SNR. First, the factor is calculated to measure the spectrum overlap degree between the residue and the denoised signal in every stage. Then, the curve of the SOF with respect to the number of stages is analyzed. Further, the minimum of the SOFs, which indicates the less spectrum overlap, allows for the estimation of the optimal stage. The proposed strategy avoids inappropriate parameter selection effectively since the estimation of the optimal stage is automatic. Besides, simulation results show that the proposed method has satisfactory denoising performance in different test scenarios.