Blind Separation of Signals in Chaotic Background Based on Cat Swarm Algorithm

The mixed signal generated by the superposition of chaotic signal and deterministic small signal is a higher dimensional chaotic signal, so it can not be separated by the general method of noise suppression of chaotic signal. This paper presents a blind separation method. Unlike the existing independent component analysis method which only takes advantage of the statistical characteristic of mixed signals to address separation issues, this method can make full use of the intrinsic dynamic property of mixed signals so that better separation effect can be obtained when deterministic signals like chaotic signals are processed. In addition, the parametric representation of orthogonal matrices effectively reduces the complexity of blind separation, making the optimization process converge quickly. The experimental results show that the overall performance of this method which is of faster convergence speed and higher numerical accuracy.