Abstract
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.
Highlights
Throughout the world, researchers and engineers are developing intelligent robotic systems for manufacturing, service, and medicine and health care
Inspired by the Basis Pursuit Denoising (BPD) formulation we propose a SALSAbased algorithm to modify modes extracted from measurements y(n) with the use of Variational Mode Decomposition
In this study we test the performance of the proposed method based on simulated signals
Summary
Throughout the world, researchers and engineers are developing intelligent robotic systems for manufacturing, service, and medicine and health care. The intelligent robotic systems required algorithms and methods to process acquired data from sensors and to control operating actuators. Time-domain methods include local and non-local filters These filters exploit the similarities between statistics of different regions in the signal. The basic assumption behind sparsity-based denoising is that the signal can be sparsely represented in a transform domain but the noise cannot. Variational Mode Decomposition (VMD) is an adaptive and non-recursive signal analysis method [15]. In [21] the authors combine the Variational Mode Decomposition method and the LSTM (long short-term memory) network to solve the problem of forecasting the non-ferrous metal price. A sparse signal processing is applied to design a new denoising algorithm based on Variational Mode Decomposition. The general idea of the proposed approach is to apply the Split Augmented Lagrangian Shrinkage Algorithm algorithm to each mode extracted from the signal with the use of the VMD algorithm
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