Reconstruction Error in Nonuniformly Sampled Approximately Sparse Signals
| Autor: | Milos Dakovic, Cornel Ioana, Isidora Stankovic, Milos Brajovic, Ljubisa Stankovic |
|---|---|
| Přispěvatelé: | University of Montenegro (UCG), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA) |
| Rok vydání: | 2021 |
| Předmět: |
Noise measurement
Computer science 0211 other engineering and technologies Nonuniform sampling Jitter 02 engineering and technology Domain (software engineering) symbols.namesake [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 0202 electrical engineering electronic engineering information engineering Range (statistics) Random variables Electrical and Electronic Engineering 021101 geological & geomatics engineering Sensors Sampling (statistics) Indexes 020206 networking & telecommunications Geotechnical Engineering and Engineering Geology Discrete Fourier transforms Noise Compressed sensing Fourier transform symbols Algorithm Random variable |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2021, 18 (1), pp.28-32. ⟨10.1109/LGRS.2020.2968137⟩ |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2020.2968137 |
| Popis: | International audience; With its aim to reduce the amount of sensed data and to improve the energy efficiency, compressive sensing (CS) is recently witnessing a growing research interest in remote-sensing applications. The Fourier transform domain plays a significant role as a signal-processing tool and the sparsity domain for the CS-reconstruction methods. A generalized expression for the error in the reconstruction of nonuniformly sampled, approximately sparse, or nonsparse, noisy signals in the Fourier domain is presented in this letter. This expression holds for a wide range of practically important nonuniform signal-sampling strategies, covering the uniform and completely random sampling as the special cases. Additive noise and noise-folding effects are included in the analysis. Statistical examples and two real-world examples validate the presented theory. |
| Databáze: | OpenAIRE |
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