基于峰度准则与判决引导的非线性盲解卷积  

Nonlinear Blind Deconvolution Based on Kurtosis Criterion and Decision Directed Algorithm

作　　者：何同林[1] 郑鹏[2] 刘郁林[3] 尤春艳[3] 

机构地区：[1]重庆工程职业技术学院计算机系 [2]78046部队 [3]重庆通信学院DSP实验室

出　　处：《电子科技大学学报》2007年第2期186-189,共4页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金资助项目(60672157;60672158)

摘　　要：针对Weiner模型,提出了一种基于最大峰度准则与判决引导相结合的非线性系统盲解卷积算法。在代价函数中引入了判决引导均方误差,优化代价函数,减少局部极值和降低剩余误差。研究了利用实数编码的遗传算法对代价函数进行最优化搜索。仿真实验表明该算法具有快速收敛性能和高精确度等优点,能够大大提高解卷积后的输出信噪比。A new blind deconvolution algorithm for Weiner model is proposed, based on kurtosis criterion and decision directed. Through analyzing when maximum kurtosis is used to resolve nonlinear blind deconvolution problem, it is found there exist some disadvantages, such as too many local optimum values and large residual error. So the decision directed least mean error is introduced in the cost function, and the number of local optimum values can be reduced and residual error is decreased. To overcome the drawback of traditional gradient search approaches, likely falling into local minimum, the real coded genetic algorithm is adopted to search the optimum solution. Simulation results demonstrate this algorithm not only has fast convergence rate and high accuracy, but also can greatly improve the output signal noise ratio.

关 键 词：盲解卷积 判决引导 遗传算法 最大峰度准则 

分 类 号：TN911.5[电子电信—通信与信息系统]