数字调制信号识别的特征参数优化方法

Feature Optimization for Digital Modulation Signals Recognition

针对数字调制信号识别中特征参数数目多和特征冗余的问题,提出一种数字调制信号识别的特征参数优化方法。首先利用正交实验对常用的20个特征参数进行优化选择,然后利用RBF神经网络识别9种数字调制信号,最后分别与主分量分析方法(PCA)和核主分量分析方法(KPCA)进行比较。仿真结果表明,该方法在高斯和多径信道下均能够有效地对特征参数进行优化选择,比PCA方法和KPCA方法有更好的优化能力。

Aiming at the problem of feature parameter numerousness and feature redundancy in the recognition of digital modulation signals,a feature optimization method for digital modulation signals recognition was proposed in this paper.Firstly,the method optimized the feature parameters of twenty selected features by orthogonal experiment.And then it recognized nine kinds of digital modulation signals using RBF neural network.Finally,the method compared to PCA method and KPCA method.The simulation results show that the method is able to optimize feature parameters effectively in Gaussian and multipath channel,and has much better optimization ability than PCA and KPCA methods.