In this paper, a new on-line inverse scattering methodology is proposed. The original problem is recast into a regression estimation one and successively solved by means of a support vector machine (SVM). Although the approach can be applied to various inverse scattering applications, it results very suitable to deal with the buried object detection. The application of SVMs to the solution of such kind of problems is firstly illustrated. Then, some examples, concerning the localization of a given object from scattered field data acquired at a number of measurement points, are presented. The effectiveness of the SVM method is evaluated also in comparison with classical neural networks (NNs) based approaches. (c) 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

An Innovative Real-Time Technique for Buried Object Detection / Bermani, Emanuela; Caorsi, Salvatore; Massa, Andrea; Boni, Andrea. - ELETTRONICO. - (2003), pp. 1-7.

An Innovative Real-Time Technique for Buried Object Detection

Bermani, Emanuela;Massa, Andrea;Boni, Andrea
2003-01-01

Abstract

In this paper, a new on-line inverse scattering methodology is proposed. The original problem is recast into a regression estimation one and successively solved by means of a support vector machine (SVM). Although the approach can be applied to various inverse scattering applications, it results very suitable to deal with the buried object detection. The application of SVMs to the solution of such kind of problems is firstly illustrated. Then, some examples, concerning the localization of a given object from scattered field data acquired at a number of measurement points, are presented. The effectiveness of the SVM method is evaluated also in comparison with classical neural networks (NNs) based approaches. (c) 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
2003
Trento
University of Trento - Dipartimento di Ingegneria e Scienza dell'Informazione
An Innovative Real-Time Technique for Buried Object Detection / Bermani, Emanuela; Caorsi, Salvatore; Massa, Andrea; Boni, Andrea. - ELETTRONICO. - (2003), pp. 1-7.
Bermani, Emanuela; Caorsi, Salvatore; Massa, Andrea; Boni, Andrea
File in questo prodotto:
File Dimensione Formato  
DISI-11-012.R51.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 205.51 kB
Formato Adobe PDF
205.51 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/358233
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact