基于最大峰度准则和遗传算法的盲辨识与盲均衡

根据最大峰度准则设计了一种针对线性系统的盲辩识与盲均衡算法。该算法在对系统参数进行估计的同时，用求逆滤波器的方法估计均衡器系数，并利用最大峰度准则不断调整系统参数的估计值，使其逼近实际值。由于采用了高阶累积量，算法对高斯噪声有较好的抑制能力。针对传统梯度搜索方法容易陷入局部收敛问题，又提出利用实数编码的遗传算法对准则函数进行最优化搜索。仿真实验表明，本算法具有快速收敛性能和高精确度等优点，能够大大提高均衡后的输出信噪比。

Blind identification and equalization based on maximum kurtosis criteria and genetic algorithm

Based on maximum kurtosis criteria, a new blind identification and equalization algorithm is designed for linear system. When system parameters are evaluated, the equalizer parameters can also be obtained at the same time by searching inverse filter parameters. The evaluated values of system parameters are constantly regulated by maximum kurtosis criteria so as to approach the real values. Because of the utilization of high order cumulant, this algorithm can effectively suppress Gaussian noise. The real coded genetic algorithm is also proposed to search the optimum solution, which can overcome the drawback of traditional gradient search technique which is likely to fall in local minimum. Simulation results demonstrate that the algorithm not only has a fast convergence performance and high accuracy, but also can improve the output SNR greatly.