Research of noise reduction of underwater acoustic signals based on singular spectrum analysis

Noise reduction of underwater acoustic signals radiated by ships and other underwater vehicles plays an important role in both signal detection and feature extraction.Especially for extracting correctly nonlinear characteristic parameters of underwater acoustic signals,which are Lyapunov exponent,fractal dimention and information entrop.The results of noise reduction can severely affect the results of further processing.Singular value decomposition(SVD) which is very popular in matrix theory is used to achieve satisfactory noise reduction of underwater acoustic signals.Firstly,the phase space reconstructions of real signals collected from the sea are established according to Takens theorem.And then the covariance matrix derived from the phase space reconstruction is gotten.After performing SVD for covariance matrix,a singular spectrum which corresponds to the signal energy and noise level is obtained.Using this noise level the distinction between the deterministic signals and random noise can be telled.Twenty samples for each of three different types of real ship signals are chosen to perform noise reduction.The results show that the algorithm is effective and a much more cleaned signal can be obtained from this method.