摘要
针对数字调制识别在低信噪比下的应用,提出了一种基于联合特征参数的数字调制识别优化算法。该算法利用调制信号的高阶累积量和时域瞬时信息,并结合星座图特征进行特征提取,采用弹性反向传播(resili-ent back-propagation,RPROP)算法训练的反向传播(back propagation,BP)神经网络对多进制数字幅度调制(M-ary amplitude shift keying,MASK)、多进制数字频率调制(M-ary frequency shift keying,MFSK)、多进制数字相位调制(M-ary phase shift keying,MPSK)、多进制正交幅度调制(M-ary quadrature amplitude modulation,MQAM)共4类12种信号进行分类识别。仿真结果表明,当信噪比低至-2dB时,提出的调制识别优化算法可使12种数字调制信号的正确识别率均达97%以上,极大地改善了低信噪比下的识别性能。
A new automatic digital modulation recognition algorithm based on combined feature parameters is proposed for the application under the low signal-to-noise ratio （SNR）. The feature parameters picked up from high order cumulants, instantaneous information and constellation characters of the signals are used as the classification vectors. The method can identify four classes of digital signals which are M-ary amplitude shift keying （MASK）, M-ary frequency shift keying （MFSK）, M-ary phase shift keying （MPSK） and M-ary quadrature amplitude modulation （MQAM） by using a back propagation （BP） neural network with the resilient back- propagation （RPROP） training algorithm as the classifier. The computer simulations show that this proposed algorithm effectively improves the practicability because of an overall success rate of 97O/oo at the SNR of --2 dB.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第12期2732-2736,共5页
Systems Engineering and Electronics
基金
中央高校基本科研业务费(CDJZR10160011)
重庆市自然科学基金(2010BB2049)资助课题
关键词
信息处理技术
数字调制识别
高阶累积量
瞬时信息
神经网络
information processing
digital modulation recognition
high order cumulant
instantaneous information
neural network
