结合时域分析和改进双谱的通信信号特征提取算法

传统的矩形积分双谱特征提取存在以下不足:第一是在以往的研究中没有讨论过积分路径个数对识别率的影响；第二是在矩形积分双谱算法中存在着部分积分路径对识别效果贡献不足、甚至带来负作用的缺点。为克服这些问题，本文提出了一种基于改进双谱和时域分析相结合的通信信号个体识别方法，首先通过实验得到了积分路径和识别率的性能曲线，选定最佳积分路径个数；其后利用最大能量区间比重算法剔除掉对识别效果贡献不足、具有负作用的积分路径；最后结合信号的时域特征并利用支持向量机分类器进行个体识别。本文用了在较低信噪比环境下的实际信号验证了提出算法，实验结果表明，该方法能够较好解决同类辐射源信号的个体识别问题，平均正确识别率高于95%。 

Extraction Algorithm of Communication signal Characteristics Basedon Improved Bispectra and Time-domain Analysis

Conventional SIB(Square Integral Bispectra,SIB) methods for feature extraction have several shortcomings: Firstly, Previous studies have not discussed the influence about integral path number on recognition rate. Secondly,there are some negative-effect integral paths.To overcoming these disadvantages,we proposed a new algorithm based on Improved Bispectra and Time-domain Analysis.First of all, The performance curve of integral path and recognition is obtained by exoeriments.Then using the largest proportion of energy inteval algorithm to remove low-contribution and negative-effect bispectrum values. Finally, the improved SIB and parameters significant for classification of the received signal formed the identification feature vector, and SVM(Support Vector Machine,SVM) was used to realize the individual identification. Experiment results show that the method is able to classify the same model transmitter with an accuracy rate of no less than 95% under the environment of lower SNR.