基于IPOA-VMD与改进小波阈值的视频电磁泄漏信号处理方法

Research on Signal Processing Methods for Electromagnetic Leakage from Computer Video

摘要:

计算机显示设备无意发射的电磁波中包含着有价值的信息，但其在传播过程中极易受噪声干扰，进而严重影响泄漏信息的恢复效果。为此，提出一种改进鹈鹕优化变分模态分解结合小波阈值去噪的视频电磁泄漏信号预处理方法，以此来抑制噪声干扰的影响。该方法通过改进POA算法优化VMD的模态数和惩罚因子，对分解出的模态分量进行余弦相似度分析以筛选有效成分，并采用改进的小波阈值算法对所选模态做进一步降噪处理。仿真结果表明，该方法在信噪比、均方根误差和相关系数等指标上均优于POA-VMD重构及POA-VMD-小波阈值方法。在实际泄漏信号实验中，多组数据处理结果显示所提方法平均噪声抑制比达到18.4924dB，较对比方法分别提升了2.9567dB、1.2685dB,并进行图像重建实验验证了所提方法可以提升电磁泄漏信息的还原效果。

Abstract:

The EM waves unintentionally emitted by computer displays contain valuable information, but are highly susceptible to noise during propagation, degrading the recovery of leaked data. To suppress this noise, a preprocessing method combining an improved Pelican Optimization Algorithm (POA)-VMD with wavelet threshold denoising is proposed. The improved POA optimizes the parameters for VMD. The decomposed modes are then screened via cosine similarity, and effective components are further denoised using an enhanced wavelet threshold algorithm. Simulations show the proposed method outperforms both POA-VMD reconstruction and POA-VMD-wavelet methods in signal-to-noise ratio, root mean square error, and correlation coefficient. Practical tests on multiple leakage signal datasets yield an average noise suppression ratio of 18.4924 dB, representing improvements of 2.9567 dB and 1.2685 dB over the contrasting methods. Image reconstruction experiments confirm the method"s effectiveness in restoring electromagnetic leakage information.