Robust GNC approach for quantised compressed sensing
| Autor: | Fatma Abdelkefi, I. Elleuch, M. Siala, R. Hamila, N. Al-Dahir |
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| Rok vydání: | 2017 |
| Předmět: |
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Approximation theory Scale (ratio) Optimal estimation Computer science 020206 networking & telecommunications 02 engineering and technology 01 natural sciences Compressed sensing Compressed Sensing Distortion Quantization 0103 physical sciences Path (graph theory) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 010306 general physics Algorithm Bits |
| Zdroj: | Electronics Letters. 53:1306-1308 |
| ISSN: | 1350-911X 0013-5194 |
| DOI: | 10.1049/el.2017.0925 |
| Popis: | Practical acquisition of compressed sensing measurements involves a finite-range finite-precision quantisation step. To solve the sparse recovery problem and handle the quantisation distortion, this Letter proposes a non-smooth graduated-non-convexity approach that follows a path of gradually improved solutions along a sequence of non-smooth non-convex optimisation problems that progressively promote quantisation consistency (QC) and sparsity. We consider two classes of multi-scale continuous approximation functions to depict intermediate QC degrees and sparsity-inducing strengths, respectively, and apply recent proximal splitting methods to solve the resulting subproblem at each refinement scale. The simulations demonstrate the convergence of intermediate solutions to a nearly optimal estimation, in terms of accuracy and support recovery. Scopus |
| Databáze: | OpenAIRE |
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