Akademik Veri Yönetim Sistemi
Multiple Criteria Decision Making: Beyond the Information Age, Y.I. Topcu,Ö. Özaydın,Ö. Kabak,Ş. Önsel Ekici, Editör, Springer, London/Berlin , London, ss.1-35, 2021
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, the classification phase of an ATR system
having heterogeneous sensors is considered. We propose novel multiple criteria
classification methods based on the 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 a higher accuracy
ratio is chosen for each of the sensors. 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.