Compressive Sensing Based Candidate Detector: Applications to Spectrum Sensing and Through-the-Wall Radar Imaging - Couverture souple

Eva, Lagunas

 
9783330009509: Compressive Sensing Based Candidate Detector: Applications to Spectrum Sensing and Through-the-Wall Radar Imaging
Couverture souple
ISBN 10 : 3330009500 ISBN 13 : 9783330009509
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. Unlike majority of CS literature, the proposed PhD thesis surveys the CS theory applied to signal detection, estimation and classification, which not necessary requires perfect signal reconstruction or approximation. In particular, a novel CS-based detection technique which exploits prior information about some features of the signal is presented. The basic idea is to scan the domain where the signal is expected to lie with a candidate signal estimated from the known features.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. Unlike majority of CS literature, the proposed PhD thesis surveys the CS theory applied to signal detection, estimation and classification, which not necessary requires perfect signal reconstruction or approximation. In particular, a novel CS-based detection technique which exploits prior information about some features of the signal is presented. The basic idea is to scan the domain where the signal is expected to lie with a candidate signal estimated from the known features.

Biographie de l'auteur

Eva Lagunas (S'09-M'13) received the M.Sc. and Ph.D. degrees in telecommunications engineering from UPC, Barcelona, Spain, in 2010 and 2014, respectively. In 2012, she held a visiting research appointment at the Center for Advanced Communications (CAC), Villanova, PA, USA. In 2014, she joined University of Luxembourg as Research Associate.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
3330009500
ISBN 13 
9783330009509
Reliure
Broché
Nombre de pages
184
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne