基于小波变换和支持矢量机的调制信号识别

提出一种基于小波变换和支持矢量机的数字信号自动调制识别新方法，即将信号小波变换后提取各尺度上的能量峰值作为特征向量，利用支持矢量机把分类特征向量映射到一个高维空间，并在高维空间中构造最优分类超平面以实现信号分类。这种方法对高斯噪声具有良好的稳健性，并避免了神经网络中的过学习和局部极小点等缺陷。计算机仿真结果表明，这种方法具有很高的分类性能和良好的稳健性。

Modulation Signals Recognition Based on Wavelet Transform and Support Vector Machines

A new method for modulation recognition for digital communication signals based on wavelet transforms and support vector machines is presented.The energy peak value of wavelet transform scales is used as the classification vectors.SVM maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in space to realize signal recognition.This method is robust to Gaussian noise and avoids overfitting and local minimum in neural networks.The high performance and robustness of the algorithm are proved by computer simulation.