针对距离欺骗干扰辐射源的个体识别问题, 提出利用辐射源个体的包络前沿差异进行干扰源个体识别。首先, 通过先小波去噪再滑窗处理的组合去噪方法, 相比于单一的去噪处理, 能得到更好的去噪效果;然后, 采用互相关算法的思想实现接收信号与模板信号的位置对齐; 最后, 引入包络上升沿的差异幅值的均值作为特征因子, 并通过K-means聚类算法实现辐射源个体的分类。仿真结果表明文中算法比文献[10]提出的夹角余弦算法具有更好的识别效果。

To solve the problem of individual identification of distance deception jamming emitters, a new method for individual identification of jamming sources based on envelope front difference characteristics of the individual emitters is proposed. Firstly, the signal is denoised by using a combination of wavelet denoising and sliding window processing. Compared with the denoising processing only using one method, it can obtain better denoising effects. Then, the idea of cross-correlation algorithm is used to realize the position alignment between the received signal and the template signal. Finally, the mean of the difference amplitude of the envelope rising edge is introduced as the characteristic factor, and the classification of individual emitters is realized by using K-means clustering algorithm. The simulation result shows that the proposed method performs better on identification than the included angle cosine algorithm proposed in reference [10].