SVM-based Strategies as applied to Electromagnetics

In the framework of the electromagnetic approaches based on learning-by-example (LBE) techniques, this thesis focuses on the development of a strategy for the solution of complex problems by means of support vector machine (SVM). The proposed instance-based classification method compared to more traditional optimization techniques solves the arising quadratic optimization problem with constraints in a simple and reliable way leveraging on the statistical learning theory which enables the design of optimal classifiers with a solid theoretical framework. A set of input/output relations representing the training dataset permits to avoid the a-priori knowledge about the system. By exploiting the generalization capabilities, the robustness against noise and the real-time performance, this technique has been proven to be suitable for more than one real-world application. The investigated problems are addressed by integrating the measured electromagnetic field with a suitably defined classifier that is aimed at defining a real-time reconstruction of the observed domain. For each application field a set of numerical results have been reported in order to assess the effectiveness and flexibility of the proposed approach. The real-time capabilities as well as the feasibility when dealing with real data have been also verified by means of an experimental setup for the passive tracking of non-cooperative targets moving throughout the investigated area.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2010

Author dc:creator

• Viani, Federico

Contributors dc:contributor

• Massa, Andrea

• Settore ING-INF/02 - Campi Elettromagnetici

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Tutti i diritti riservati (All rights reserved)

Language dc:language

eng

Identifier

http://dx.doi.org/10.15168/11572_368063
10.15168/11572_368063

OAI identifier oai:identifier

oai:iris.unitn.it:11572/368063