Treffer: IterML: A Fast, Robust Algorithm for Estimating Signals With Finite Rate of Innovation

Title:
IterML: A Fast, Robust Algorithm for Estimating Signals With Finite Rate of Innovation
Authors:
WEIN, Alex, SRINIVASAN, Lakshminarayan
Source:
IEEE transactions on signal processing. 61(21-24):5324-5336
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2013.
Publication Year:
2013
Physical Description:
print, 14 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, Algorithme déterministe, Deterministic algorithms, Algorithme glouton, Greedy algorithm, Algoritmo glotón, Algorithme rapide, Fast algorithm, Algoritmo rápido, Complexité calcul, Computational complexity, Complejidad computación, Débit information, Information rate, Índice información, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Erreur quadratique moyenne, Mean square error, Error medio cuadrático, Evaluation performance, Performance evaluation, Evaluación prestación, Innovation, Innovación, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Méthode Prony, Prony method, Méthode stochastique, Stochastic method, Método estocástico, Robustesse, Robustness, Robustez, Simulation, Simulación, Traitement signal, Signal processing, Procesamiento señal, Transmission information, Information transmission, Transmisión información, Finite rate of innovation, Prony's method, annihilating filter method, maximum likelihood estimation, sampling, stochastic algorithms
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Neural Signal Processing Laboratory, Department of Radiology, University of California, Los Angeles, CA 90095-1721, United States
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28150122
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.28150122
Database:
PASCAL Archive

Weitere Informationen

Recently, various methods have emerged for sub-Nyquist sampling and reconstruction of signals with finite rate of innovation (FRI). These methods seek to sample parametric signals at close to their information rate and later reconstruct the parameters of interest. Some proposed reconstruction algorithms are based on annihilating filters and root-finding. Stochastic methods based on Gibbs sampling were subsequently proposed with the intent of improving robustness to noise, but these may run too slowly for some real-time applications. We present a fast maximum-likelihood-based deterministic greedy algorithm, IterML, for reconstructing FRI signals from noisy samples. We show in simulation that it achieves comparable or better performance than previous algorithms at a much lower computational cost. We also uncover a fundamental flaw in the application of MMSE (minimum mean squared error) estimation, a technique employed by some existing methods, to the problem in question.