Blind source separation algorithm for communication complex signals in communication reconnaissance

Abstract

Most blind source separation algorithms are only applicable to real signals, while in communication reconnaissance processed signals are complex. To solve this problem, a blind source separation algorithm for communication complex signals is deduced, which is obtained by adopting the Kullback-Leibler divergence to measure the signals' independence. On the other hand, the performance of natural gradient is better than that of stochastic gradient, thus the natural gradient of the cost function is used to optimize the algorithm. According to the conclusion that the signal's mixing matrix after whitening is orthogonal, we deduce the iterative algorithm by constraining the separating matrix to an orthogonal matrix. Simulation results show that this algorithm can efficiently separate the source signals even in noise circumstances.

A geometric approach to blind separation of nonnegative and dependent source signals

“…There has been a considerable interest for separating source signals blindly because such a situation occurs in numerous fields such as analytical chemistry [2], communication [3], data mining [4], medical sciences [5]. .…”