Result: Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements

Title:
Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements
Authors:
LASKA, Jason N, ZAIWEN WEN, WOTAO YIN, BARANIUK, Richard G
Source:
IEEE transactions on signal processing. 59(11):5289-5301
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2011.
Publication Year:
2011
Physical Description:
print, 31 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Echantillonnage, quantification, Sampling, quantization, Algorithme, Algorithm, Algoritmo, Circuit comparateur, Comparator circuit, Circuito comparador, Convertisseur AN, AD converter, Convertidor AN, Détection signal, Signal detection, Detección señal, Effet non linéaire, Non linear effect, Efecto no lineal, Intervalle confiance, Confidence interval, Intervalo confianza, Phénomène non linéaire, Non linear phenomenon, Fenómeno no lineal, Programmation non convexe, Non convex programming, Programación no convexa, Quantification signal, Signal quantization, Cuantificación señal, Rapport signal bruit, Signal to noise ratio, Relación señal ruido, Reconstruction signal, Signal reconstruction, Reconstrucción señal, Restauration signal, Signal restoration, Robustesse, Robustness, Robustez, Taux échantillonnage, Sampling rate, Razón muestreo, Traitement signal, Signal processing, Procesamiento señal, Echantillonnage parcimonieux, Compressed sensing, 1-Bit compressive sensing, consistent reconstruction, quantization, trust-region algorithms
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical and Computer Engineering, Rice University, Houston, TX, 30332, United States
Department of Mathematics and Institute of Natural Sciences, Shanghai Jiaotong University, Shanghai, China
Department of Computational and Applied Mathematics, Rice University, Houston, TX 30332, United States
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24707411
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.24707411
Database:
PASCAL Archive

Further Information

The recently emerged compressive sensing (CS) framework aims to acquire signals at reduced sample rates compared to the classical Shannon-Nyquist rate. To date, the CS theory has assumed primarily real-valued measurements; it has recently been demonstrated that accurate and stable signal acquisition is still possible even when each measurement is quantized to just a single bit. This property enables the design of simplified CS acquisition hardware based around a simple sign comparator rather than a more complex analog-to-digital converter; moreover, it ensures robustness to gross nonlinearities applied to the measurements. In this paper we introduce a new algorithm—restricted—step shrinkage (RSS)—to recover sparse signals from 1-bit CS measurements. In contrast to previous algorithms for 1-bit CS, RSS has provable convergence guarantees, is about an order of magnitude faster, and achieves higher average recovery signal-to-noise ratio. RSS is similar in spirit to trust-region methods for nonconvex optimization on the unit sphere, which are relatively unexplored in signal processing and hence of independent interest.