Blind calibration for compressed sensing: State evolution and an online algorithm - Archive ouverte HAL Access content directly
Journal Articles Journal of Physics A: Mathematical and Theoretical Year : 2020

Blind calibration for compressed sensing: State evolution and an online algorithm

(1) , (2) , (1) , (3)
1
2
3

Abstract

Compressed sensing allows the acquisition of compressible signals with a small number of measurements. In experimental settings, the sensing process corresponding to the hardware implementation is not always perfectly known and may require a calibration. To this end, blind calibration proposes to perform at the same time the calibration and the compressed sensing. Schülke and collaborators suggested an approach based on approximate message passing for blind calibration (cal-AMP) in [1, 2]. Here, their algorithm is extended from the already proposed offline case to the online case, for which the calibration is refined step by step as new measured samples are received. We show that the performance of both the offline and the online algorithms can be theoretically studied via the State Evolution (SE) formalism. Finally, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed through numerical simulations.
Fichier principal
Vignette du fichier
1910.00285.pdf (1.03 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

cea-02529311 , version 1 (02-04-2020)

Identifiers

Cite

Marylou Gabrié, Jean Barbier, Florent Krzakala, Lenka Zdeborová. Blind calibration for compressed sensing: State evolution and an online algorithm. Journal of Physics A: Mathematical and Theoretical, 2020, ⟨10.1088/1751-8121/ab8416⟩. ⟨cea-02529311⟩
37 View
51 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More