支持向量机多类分类的数字调制方式识别

针对神经网络存在的过学习、欠学习、局部极小值等问题,提出了一种基于支持向量机(SVM)的数字调制方式的识别方法。从信号的瞬时幅度,瞬时相位,瞬时频率,频谱,包络变化等特性中提取了7个特征参数,用于训练支持向量机。运用二叉树理论设计多类分类器,与已有算法相比,具有简单、高速、高精度的特点。仿真结果证明,在高斯白噪声(AWGN)下,当信噪比大于15dB时,对2ASK、4ASK、8ASK、2FSK、4FSK、8FSK、BPSK、QPSK、8PSK调制方式的识别率可以达到97% 以上。

Digital modulation mode recognition based on multi-classclassification of support vector machine

To solve the overfitting, underfitting and local minimum existing in neural networks, a digital modulation mode recognition method based on support vector machine (SVM) is proposed. Seven characteristic parameters are extracted from instantaneous amplitude, instantaneous phase, instantaneous frequency, frequency spectrum, and changes in characteristics of the envelope to train support vector machine. Compared with the existing algorithms, using binary tree theory to design multi-class classifier has the features of simple, high-speed, high-precision. The simulation results indicate that the scheme can achieve 97% recognition accuracy when the signal to noise ratio (SNR) is above 15 dB with the AWGN channel.