基于同步压缩小波变换的主信号抑制技术

在辐射源个体识别(SEI)技术中，能量较高的主信号往往导致微弱个体特征稳定性降低，进而影响最终的个体识别效果。为了解决该问题并提升辐射源个体识别性能，该文提出基于同步压缩小波变换的主信号抑制技术。首先，利用静态小波变换完成对带噪信号的去噪预处理；然后，利用同步压缩小波变换完成对主信号的检测和抑制，并以均方根误差和皮尔逊相关系数为数值指标，验证算法的有效性；最后，在主信号抑制的基础上，利用分形理论中盒维数完成对信号的特征提取，并利用单核支持向量机验证个体识别性能。实验结果表明，与主信号抑制之前相比，主信号抑制算法下个体识别率提升了10%左右，验证了同步压缩小波变换的主信号抑制算法对辐射源个体识别率提升的有效性。

Primary Signal Suppression Based on Synchrosqueezed Wavelet Transform

In Specific Emitter Identification (SEI), the stability of individual features and final correct identification rate are always declined due to the influence of the primary signal with high energy on the individual features. To solve the problem above, a primary signal suppression algorithm based on synchrosqueezed wavelet transform is exploited for specific emitter identification in this paper. Firstly, a denoising method based on stationary wavelet transform is applied to preprocess the noised signal; Then, the detection and suppression of the primary signal from time-frequency distribution are developed, where root mean square error and Pearson correlation coefficient are used as numerical indicators to measure the effectiveness of the proposed primary signal suppression algorithm; Finally, a feature extraction based on box-counting dimension and a classification based on support vector machine are exploited to verify the identification performance. The simulation results show that the correct identification rate of SEI using the proposed primary signal suppression outperforms the conventional SEI with 10%, which proves the practical improvement of the proposed primary signal suppression algorithm on specific emitter identification.