特征提取及其在数字调制方式识别中的应用

通信信号调制识别技术在军事和民用领域都具有重要的应用前景,而特征参数的提取是调制方式识别的首要问题。本文提出了两个新的特征参数,并结合这两个新的特征参数在统计模式识别的基础上构造了一组新的特征集参数,该特征集无需任何先验知识。随后,本文针对2PSK、QPSK、8PSK、16QAM、2FSK、4FSK、8FSK、2ASK、4ASK等9种调制类型,采用了分层结构的神经网络分类器进行自动识别。大量仿真表明,在待识别的信号信噪比大于5dB时,该识别系统的正确识别率达97%以上,且识别的稳定性好。

Feature Extraction and Its Application to Digital Modulation Recognition

Digital modulation recognition has found important applications both in military and civil,while the feature extraction is the preliminary step for digital modulation recognition.This paper proposes two novel features,and combine them and other existing pa- rameters to construct a new feature set based on statistic pattern recognition.This set of features needs no a priori information about the nature of the signal.Later on,we adopt a layered neural network classifier to complete the recognition process.The modulation types clas- sifted by this recognizer are 2PSK,QPSK,8PSK,2FSK,4FSK,8FSK,2ASK,4ASK and 16QAM.Simulation results show that the average rate of accurate classification is over 97% at a signal-to-noise(SNR)of 5dB,and more,the system is robust.