The paper proposes an approach to signal denoising based on a combination of Variational Mode Decomposition with the Split Augmented Lagrangian Shrinkage Algorithm.

In our research, we found that the proposed approach gives a great improvement of denoising gyroscopic signals. In turn, the results for the synthetic signals are not straightforward. For the bumps synthetic signals, the proposed algorithm gives the best results for different levels of signal degradation. While for the Doppler and blocks synthetic signals the reference methods give better results. However, for heavisine test signal the proposed algorithm gives better results in almost all cases.

A weak point of the presented algorithm is its time complexity. The proposed approach is based on the Split Augmented Lagrangian Shrinkage Algorithm, which is the iterative optimization method since the time of computation strongly depends on the number of iterations.

The presented results show that the proposed approach gives a great improvement in signal denoising and it is a promising direction of future research.

提出了一种基于变分模式分解和分裂增强拉格朗日收缩算法相结合的信号去噪方法。

在我们的研究中，我们发现所提出的方法极大地改善了陀螺信号的去噪效果。反过来，合成信号的结果并不简单。对于颠簸的合成信号，该算法针对不同程度的信号衰减给出了最佳结果。对于多普勒和块合成信号，参考方法可提供更好的结果。然而，对于重载测试信号，所提出的算法在几乎所有情况下都能提供更好的结果。

所提出算法的弱点是其时间复杂度。所提出的方法基于分裂增强拉格朗日收缩算法，该算法是迭代优化方法，因为计算时间很大程度上取决于迭代次数。

结果表明，所提出的方法在信号降噪方面有很大的改进，是未来研究的有希望的方向。