In this paper

we propose a signal extraction algorithm based on the property of chaotic signal.Each chaotic signal corresponds to a different chaotic attractor in phase space.We define proliferation exponent(PE) using the property above;PE is used as a statistic feature to classify chaotic signal and computationally less dissipative compared with Kullback Leibler divergence.We firstly model the problem of blind source extraction into an optimization problem with constrained.The objective function based on PE is non-convex or multi-model and solving the optimization problem with gradient search method may lead to local optimum.We use particle swarm optimization(PSO) algorithm to solve the above optimization problem

the algorithm is improved by adjusting the inertia coefficient dynamically and the global optimal position is disturbed to diversify the particle population and increase the probability of escaping from local trap.The experimental results show that the proposed signal extraction algorithm can extract the mixture of chaotic signals and multi-channel Gaussian signals efficiently.