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数字通信信号调制方式识别算法的改进研究
引用本文:李娜,高宪军,田润澜.数字通信信号调制方式识别算法的改进研究[J].吉林大学学报(信息科学版),2010,28(3):250-255.
作者姓名:李娜  高宪军  田润澜
作者单位:空军航空大学,航空电子工程系,长春,130022;吉林大学,通信工程学院,长春,130025;空军航空大学,航空电子工程系,长春,130022
摘    要:为克服数字通信信号调制方式识别算法识别类型少,步骤复杂,识别率低等问题,在已有识别算法的基础上,通过对信号特征参数的分析和提取,提出一种基于决策理论的数字通信信号调制样式识别的改进算法。该算法通过比较不同信噪比下特征参数的取值概率直方图,选择判决门限值。同时,应用最大似然法则,并采用了可变的判决门限值,以得到最佳判决门限。研究结果表明,在信噪比(SNR:Signal to Noise Ratio)为10 dB时,算法的正确识别率达到96%以上,可识别包括噪声在内的7种信号,且信噪比为6~15 dB时,该算法的正确识别率不低于92%。

关 键 词:调制方式识别  特征参数  判决门限  最大似然法则

Research on Improving the Identification Algorithm of Digital Communication Signals
LI Na,GAO Xian-jun,TIAN Run-lan.Research on Improving the Identification Algorithm of Digital Communication Signals[J].Journal of Jilin University:Information Sci Ed,2010,28(3):250-255.
Authors:LI Na  GAO Xian-jun  TIAN Run-lan
Affiliation:1Department of Aviation Electronical Engineering,Aviation University of Air Force, Changchun 130022|China;
2College of Communication Engineering, Jilin University, Changchun 130025|China
Abstract:By statistics of instantaneous characteristic and analysis of characteristic parameters, an identification method was obtained, which is used to recognize the digital communication signal modulation based on the decision theory and the previous research of Azzouzi. The new method solves the probloms,of small identify types, complex procedures and low recognition rate. The new method has great practical significance. The key of the algorithm is to get the best thresholds and the thresholds are variable in different SNR (Signal to Noise Ratio). The paper compares the probability of the characteristic parameters based on the ML(Maximum Likelihood) to obtain the best judge thresholds. The result indicates that this method can recognize 7 different kinds of signals including noises, and the success rate is higher than 96% when SNR is 10 dB, and the success rate is higher than 92% when the SNR is between 6 dB and 15 dB.
Keywords:modulation recognition  analysis of characteristic parameter  judge thresholds  maximum likelihood(ML)  
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