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.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/368063 |
| Date | January 2010 |
| Creators | Viani, Federico |
| Contributors | Viani, Federico, Massa, Andrea |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:116, numberofpages:116 |
Page generated in 0.0177 seconds