Synthetic-Range-Profile-Based Training Library Construction for Ship Target Recognition Purposes of Scanning Radar Systems

The quantity and quality of range profiles in a training library play a significant role in target recognition. Classifier structures should be built up by experiencing a sufficient amount of range profiles in order to enhance classification accuracy. On the other hand, there might be insufficient measuremental range profiles especially for the targets rarely cruising. Therefore, training libraries should be enriched for these types of targets. In this article, the suitability and efficiency of creating a training library with synthetic range profiles are investigated for maritime target recognition purposes of scanning radar systems. In this context, synthetic range profiles are generated for six different ship targets, and the compatibility of these profiles with measuremental ones has been analyzed from different points of view. Moreover, a novel approach on generating synthetic range profiles is introduced. In the sense of compatibility studies, first, correlation analyses have been performed directly between range profiles and then between spatial- and velocity-based features. It is seen that the proposed approach has the ability to generate more compatible range profiles than the conventional methodology. In addition to similarity analyses, classification examinations have been performed for different feature use cases by employing synthetic profiles in the training set and the measuremental ones in the test set. At this step, a $k$-nearest-neighbor-based classifier that benefits by the discrimination ability of length- and velocity-based features is proposed to obtain higher classification rates. The results of the classification examinations show that synthetic profiles could be used either to construct an offline training library from scratch or to enrich a nonhomogenous and lacking library for ship target recognition.