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Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function

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dc.contributor.author Atıcı, Bengü
dc.contributor.author Karasakal, Esra
dc.contributor.author Karasakal, Orhan
dc.date.accessioned 2021-06-16T10:25:34Z
dc.date.available 2021-06-16T10:25:34Z
dc.date.issued 2020
dc.identifier.citation Atıcı, Bengü; Karasakal, Esra; Karasakal, Orhan (2020). "Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function", Multiple Criteria Decision Making - Beyond the Information Age, Switzerland: Springer, 2020. tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/4800
dc.description.abstract Automatic Target Recognition (ATR) systems are used as decision support systems to classify the potential targets in military applications. These systems are composed of four phases, which are selection of sensors, preprocessing of radar data, feature extraction and selection, and processing of features to classify potential targets. In this study, classification phase of an ATR system having heterogeneous sensors is considered. We propose novel multiple criteria classification methods based on modified Dempster-Shafer theory. Ensemble of classifiers is used as the first step probabilistic classification algorithm. Artificial neural network and support vector machine are employed in the ensemble. Each non-imaginary dataset coming from heterogeneous sensors is classified by both classifiers in the ensemble, and the classification result that has higher accuracy ratio is chosen for each of the sensor. The proposed data fusion algorithms are used to combine the sensors' results to reach the final class of the target. We present extensive computational results that show the merits of the proposed algorithms. tr_TR
dc.language.iso eng tr_TR
dc.publisher Springer tr_TR
dc.relation.isversionof 10.1007/978-3-030-52406-7_1 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.title Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function tr_TR
dc.type bookPart tr_TR
dc.relation.journal Multiple Criteria Decision Making - Beyond the Information Age tr_TR
dc.contributor.authorID 216553 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü tr_TR


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