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Incoherence Analysis of RD-AIC-Based Observation Matrix and Its Application in Power Quality Disturbance Signal
- Source :
- Mathematical Problems in Engineering, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- In the theory of compressed sensing, restricted isometry property (RIP) decides the universality and reconstruction robustness of an observation matrix. At present, an observation matrix based on RD-AIC (RD-AIC-based observation matrix) can compress sparse continuous signals with a simple structure, but RIP analysis of this matrix is lack and challenging to prove. In this paper, this problem is relaxed. Instead, we demonstrate the incoherence analysis process, derive the orthogonality and nonsingularity of the matrix, propose the generalized relevance calculation steps of the matrix, and propose the hardware parameter design principles to improve the incoherence of the matrix. Moreover, compression and reconstruction experiments used in power quality disturbance signals are developed for testing the incoherence. The results show that the RD-AIC-based observation matrix has substantial incoherence under suitable hardware parameters, equivalent to the Gaussian random matrix and the Bernoulli random matrix.
- Subjects :
- Article Subject
Computer science
General Mathematics
Gaussian
020208 electrical & electronic engineering
General Engineering
020206 networking & telecommunications
02 engineering and technology
Engineering (General). Civil engineering (General)
Restricted isometry property
symbols.namesake
Matrix (mathematics)
Compressed sensing
Orthogonality
Robustness (computer science)
Compression (functional analysis)
0202 electrical engineering, electronic engineering, information engineering
symbols
QA1-939
TA1-2040
Random matrix
Algorithm
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15635147
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....5be161d8bf1a1e9bb241d2773e512f72