摘要
当前通信辐射源的特征识别方法不仅需要较高的样本数,而且存在识别效率低、识别率下降的问题。为此,提出一种应用Softmax回归对通信信号循环谱进行多分类识别的方法。以通信信号的循环谱密度特征为样本集,通过主成分分析降维算法筛选特征样本,使用Softmax回归多分类识别器对特征样本进行分类。实验结果表明,与传统神经网络方法相比,该方法可以实现对通信辐射源个体的有效识别,并且识别时间较短。
According to the characteristics of the current communication source identification method needs not only the higher number of samples,and identification efficiency is low,recognition rate drop,this paper proposes a Softmax regression was applied to cyclic spectrum signal classification method.The method based on cyclic spectrum density features of communication signals sample set,filtering feature samples by Principle component analysis dimensionality reduction algorithm,finally using Softmax return classification recognizer classify characteristics of samples.Experimental results show that compared with the traditional neural network algorithm,the method can achieve an efficient identification of communication sources,and the identification of a relatively short time.
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第2期98-102,共5页
Computer Engineering
基金
国家自然科学基金(61461013)
广西自动检测技术与仪器重点实验室主任基金(YQ15115)
桂林电子科技大学创新团队"广西无线宽带通信与信号处理重点实验室"2016年主任基金(GXKL06160103)
关键词
通信辐射
特征识别
Softmax回归
主成分分析
循环谱
ommunication radiation
feature identification
Softmax regression
Principal Component Analysis（PCA）
cyclic spectrum
