Ascione, Marcello (2013) Innovative Measurement methods for Homeland Security applications. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Lingua: English
Title: Innovative Measurement methods for Homeland Security applications
Creators:
CreatorsEmail
Ascione, Marcellom.ascione81@gmail.com
Date: 2 April 2013
Number of Pages: 120
Institution: Università degli Studi di Napoli Federico II
Department: Informatica e sistemistica
Scuola di dottorato: Ingegneria dell'informazione
Dottorato: Ingegneria informatica ed automatica
Ciclo di dottorato: 25
Coordinatore del Corso di dottorato:
nomeemail
Garofalo, Francescofrancesco.garofalo@unina.it
Tutor:
nomeemail
Angrisani, Leopoldoangrisan@unina.it
D'Urso, Michelemichele.durso@selex-es.com
Buonanno, Anielloaniello.buonanno@selex-es.com
Date: 2 April 2013
Number of Pages: 120
Uncontrolled Keywords: Through Wall Imaging, Measurement method, Multiple Signal Classification
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/02 - Campi elettromagnetici
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/07 - Misure elettriche e elettroniche
Aree tematiche (7° programma Quadro): SICUREZZA > Sicurezza dei cittadini
Date Deposited: 05 Apr 2013 12:40
Last Modified: 04 Dec 2014 08:25
URI: http://www.fedoa.unina.it/id/eprint/9399
DOI: 10.6092/UNINA/FEDOA/9399

Abstract

This work deals with the implementation of a suitable technological support to improve the success likelihood of Urban Search and Rescue (USAR) missions. Here we introduce a new architectural solution to detect and track humans beyond walls for homeland protection applications. Two different measurement methods have been developed, the first one for detection and tracking of moving targets in two-dimensional scenes exploiting an advanced imaging technique, which takes advantages from a regularized linear inversion scheme. The second method has been developed for life signs detection and it takes advantage from a suitable spatial smoothing strategy applied to the traditional algorithm for Multiple Signal Classification (MUSIC), mandated to single out the spectral components of the received signal.

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