Research On Blind Separation Algorithm Based On Sparse Characteristics

Blind source separation is proposed to solve the output signal obtained by the sensing array.By analyzing the output signal to recover the independent source signal in the output signal,the "blind" is reflected in the source signal cannot be directly observed and for the source.The way signals are mixed is also unknown.This approach does not rely on the establishment of the transfer function of the original system,until only a small amount of prior knowledge about the source finds the source signal from the mixed signal.This approach to multivariate data processing has been applied to data mining,speech analysis,mechanical signal fault diagnosis,radar signals,sensor arrays and image recognition.A lot of research results have been obtained in these fields,and the development of blind source separation theory has also been promoted.Theoretical practice shows that the signal itself has sparse characteristics,and the signal can exhibit sparse characteristics under a certain dictionary basis,and the sparse coefficients of different signals are sparsely expressed.This paper is based on the fact that the source signals are independent of each other and under the theoretical framework of signal thinning,it is assumed that the source signal is sparse or can be sparsely expressed.Aiming at the problem of localized convergence caused by the forced orthogonalization of the blind sparse source separation algorithm(MM-BSSS)to minimize the optimization,an improved algorithm based on the composite splitting algorithm(CSA)is proposed.The algorithm adopts the compound regularized objective function.Separation becomes two regularization problems to reduce the solution space range,and at the same time increase the sparsity of the solution,so that the recovered source signal is smoother and better separation effect,that is,the source signal can be accurately recovered.The simulation experiments show that the blind-sparse separation algorithm based on the composite splitting algorithm is better than the original algorithm.It can be used to separate the source signal from the mixed signal in audio and image signals,and avoid the error accumulation in iteration.Affects separation performance and improves the noise immunity of the improved algorithm.