Research on the Improved FastICA Blind Signal Separation Algorithm

Abstract

Blind signal separation is a mixed source separation technique for signals that are simultaneously mixed in time, space, and frequency without the complete parameters of the mixing matrix and the source signal[11]. The subject of blind signal separation of linear instantaneous mixed signals is the most studied cases at present. In this filed, a fast independent component analysis algorithm for blind separation, namely the FastICA algorithm, is mostly studied. This algorithm not only has fast convergence speed but also good separation performance, so it is widely used in practice. Through the study of the original FastICA algorithm based on maximizing negative entropy, in order to solve the problem of large calculation amount and large number of iterations in the separation process, this paper proposes an improved FastICA algorithm by combining the joint diagonalization of fourth-order matrix cumulants with the original FastICA algorithm. The simulation results show that the improved algorithm can greatly reduce the number of operation iterations and accelerate the operation speed under the premise of ensuring the separation effect.