As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
Keywords: adaptive chirp mode decomposition; feature extraction; non-local means denoising; ship-radiated noise.