基于增殖系数的混沌信号提取算法

本文提出一种基于混沌信号特性的信号盲提取算法，由于不同的混沌信号在相空间里面对应着不同的吸引子二阶增长率，利用这个特点定义了增殖系数（Proliferation Exponent，PE）并将其作为混沌信号提取的目标函数.首先分析基于增殖系数的梯度搜索方法在解决盲提取问题时存在不足，并将混沌信号的盲提取问题转化为带约束的优化问题，提出利用改进的粒子群优化算法解决信号盲提取的优化问题，通过惯性系数动态调整和最优位置的扰动，提高算法的寻优性能.实验结果表明基于增殖系数的信号提取算法能有效地提取混沌信号，提取的信号在时域和相空间与源信号接近，同时算法也表现出对噪声污染的鲁棒性.

Chaotic Signal Extraction Algorithm Based on Proliferation Exponent

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.