Underwater acoustic signal denoising based on sparse TQWT and wavelet thresholding

The time-varying characteristics of the underwater environment lead to complicated background noise in collected signals. To reduce the negative impact of noise, this paper proposes the tunable Q-factor wavelet transform (TQWT)-basis pursuit (TQWT-BP)-based wavelet thresholding denoising schemes (TQWT-BP-WT). We commence by decomposing the collected signal into multiple subbands upon using TQWT-BP. Then, subbands are divided into signal subbands and noise subbands by applying the component identification criteria. The wavelet thresholding is invoked to denoise the signal subbands, while noise subbands are discarded. To classify the subbands, we design two criteria based on the coefficients threshold and the correlation coefficients, respectively. The coefficients threshold-based criterion determines the subband components by examining the number of coefficients that satisfy the threshold conditions. By comparing the correlation coefficients between the subbands and original signal, as well as between the subbands and the pre-denoised signal, the correlation coefficient-based method distinguishes the subbands, which avoids setting the criterion threshold. In addition, we investigate the influence of different normalized signal amplitudes decomposed by TQWT-BP, and the design of the normalized scale selection approach. Finally, practical simulation results are provided to verify the performance of our proposed schemes compared to other counterparts.