A noise reduction method for communication signals of SSMD based on Lagrange multipliers and SSA

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 -14dB, the signal-to-noise ratio of the vSSMD-SSA algorithm is increased by 4.49dB 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.