Blind Source Separation Algorithm for Chaotic Masking Multipath Signals Based on Spectral Peak Search Counter Permutation

Since voice communication is the main method of information transmission, it is important to ensure the safety and efficiency of voice communication. In this paper, a more complex multipath channel model in a wireless environment is considered, and chaotic masking technology is used to ensure the security of voice signal transmission. Based on this, a multipath blind source separation algorithm based on cross-correlation permutation of spectral peak search counters is proposed. First, chaotic masking is performed between multiple voice signals and chaotic signal, and an impulse response filter is used to simulate the multipath transmission process. Next, the short-time Fourier transform is employed to convert the observation signal into a frequency-domain signal. Then, the joint approximate diagonalization of eigen-matrix algorithm is used to perform linear instantaneous blind source separation at each frequency. And to solve the permutation uncertainty problem in the frequency domain method, a cross-correlation permutation algorithm based on a spectral peak search counter is proposed. Finally, the permuted signal is transformed back to the time domain to get the estimated source signal by the inverse short-time Fourier transform. And simulation experiments show that the algorithm greatly reduces the calculation amount of the autocorrelation coefficient in the traditional permutation algorithm. Moreover, the permutation algorithm has stability and accuracy, and provides a solution for the secure transmission and effective separation of voice signals in multipath transmission channels.