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Singular value decomposition of noisy data: noise filtering
- Source :
- Experiments in Fluids. 60
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- The singular value decomposition (SVD) and proper orthogonal decomposition are widely used to decompose velocity field data into spatiotemporal modes. For noisy experimental data, the lower SVD modes remain relatively clean, which suggests the possibility for data filtering by retaining only the lower modes. Herein, we provide a method to (1) estimate the noise level in a given noisy dataset, (2) estimate the root mean square error (rmse) of the SVD modes, and (3) filter the noise using only the SVD modes that have low enough rmse. We show through both analytic and PIV examples that this method yields nearly the most accurate possible SVD-based reconstruction of the clean data. Moreover, we provide an analytic estimate of the accuracy of this reconstruction.
- Subjects :
- Fluid Flow and Transfer Processes
Mean squared error
Computational Mechanics
General Physics and Astronomy
Experimental data
Filter (signal processing)
01 natural sciences
010305 fluids & plasmas
010309 optics
Noise
Mechanics of Materials
0103 physical sciences
Singular value decomposition
Vector field
Noise level
Noisy data
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 14321114 and 07234864
- Volume :
- 60
- Database :
- OpenAIRE
- Journal :
- Experiments in Fluids
- Accession number :
- edsair.doi...........41e37ab87a956036002a31ca5e8812aa