基于神经网络的数模混合信号调制识别算法  

Modulation Recognition Algorithm of Digital and Analog Mixed Signal Based on Neural Network

作　　者：牛国庆[1,2] 姚秀娟[1] 闫毅[1] 王春梅[1] 

机构地区：[1]中国科学院空间科学与应用研究中心,北京100190 [2]中国科学院大学,北京100010

出　　处：《计算机工程》2016年第4期101-104,111,共5页Computer Engineering

基　　金：国防科工局基础科研基金资助项目

摘　　要：通信信号调制识别技术可用于信号确认、干扰识别、电子战对抗以及星间链路通信等方面。针对低噪声下信号调制方式识别率低与识别种类少的问题,提出一种基于神经网络的数字模拟混合信号调制方式识别算法。简化并改进识别特征参数,降低参数对噪声干扰的敏感度,设计基于判决树的自动识别流程。通过自适应学习速率选取最优隐含层节点数,改进BP神经网络算法。结合判决树和改进的神经网络算法,给出基于神经网络的算法调制方式识别方案。仿真结果表明,在信噪比不低于0 d B时,该算法的平均识别成功率达到98%以上。The modulation recognition technology of communication signal can be used in signal confirmation, interference identification,electronic warfare combat and intersatellite link communication. Aiming at the problem of the low signal modulation recognition rate under low Signal-to-noise-Ratio （ SNR）, an automatic modulation recognition algorithm based on neural network is proposed. By simplifying the identification feature parameters, the sensitivity of the parameters to noise is reduced and an identification process based on decision theory is presented. The BP neural network algorithm is improved by realizing adaptive learning rate and by choosing the optimal number of hidden layer nodes. Thus, an automatic modulation recognition scheme is gived based on the combination of the neural network algorithm and the decision tree. Simulation results show that, when SNR is no less than 0 dB, the average recognition ratio of the proposed algorithm is above 98%.

关 键 词：数模混合信号 调制方式识别 决策理论 神经网络 星间链路 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]