【摘要】 针对强噪声背景下通信信号的分析识别困难问题，提出一种基于拉格朗日乘子-辛奇异值模态分解(vSSMD)的奇异谱分析(SSA)降噪方法.鉴于噪声的随机变化使得采用功率谱密度方法计算嵌入维度时有较大误差，引入蒙特卡洛思想确定嵌入维数.噪声较大时，vSSMD通过构建拉格朗日乘子矩阵增强有用分量并抑制表示为噪声的残余信号，然后采用SSA方法去除vSSMD重构信号中的微弱噪声.将vSSMD-SSA算法的去噪效果与SSA、vSSMD方法进行比较，当信噪比为-14dB时，vSSMD-SSA算法相较于传统算法SSA信噪比提升了4.49dB,均方误差提升了38.25%.实验结果说明在低信噪环境比下，vSSMD-SSA算法的去噪效果最好.将vSSMD-SSA算法用于无人机通信信号去噪，降噪效果最明显.更多还原

【Abstract】 Aiming at the difficulty of analysis and identification of communication signals under strong noise background, this paper proposes a joint denoising method of Symplectic Singular Mode Decomposition based on Lagrange multiplier(vSSMD) and Singular Spectrum Analysis(SSA). Considering that the random variation of noise makes the power spectral density method to calculate the embedding dimension with large error, this paper introduces the Monte Carlo idea to determine the embedding dimension. When the noise is large, vSSMD enhances useful components and suppresses noise components by constructing a Lagrangian multiplier matrix, and then adopts the SSA method to remove the weak noise in the reconstructed signal of vSSMD. The denoising effect of the vSSMD-SSA algorithm is compared with SSA and vSSMD methods. When the signal-to-noise ratio is-14 dB, the signal-to-noise ratio of the vSSMD-SSA algorithm is increased by 4.49 dB compared with the traditional algorithm SSA, and the mean square error is increased by 38.25%. The experimental results show that under the low signal-to-noise environment ratio, the vSSMD-SSA algorithm The denoising effect is the best. The vSSMD-SSA algorithm is used to denoise the UAV communication signal, and the noise reduction effect is the most obvious.更多还原