基于峰度準(zhǔn)則與判決引導(dǎo)的非線性盲解卷積

針對(duì)Weiner模型，提出了一種基于最大峰度準(zhǔn)則與判決引導(dǎo)相結(jié)合的非線性系統(tǒng)盲解卷積算法。在代價(jià)函數(shù)中引入了判決引導(dǎo)均方誤差，優(yōu)化代價(jià)函數(shù)，減少局部極值和降低剩余誤差。研究了利用實(shí)數(shù)編碼的遺傳算法對(duì)代價(jià)函數(shù)進(jìn)行最優(yōu)化搜索。仿真實(shí)驗(yàn)表明該算法具有快速收斂性能和高精確度等優(yōu)點(diǎn)，能夠大大提高解卷積后的輸出信噪比。

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.