Paper Title
SVM based Ballistic Missile Classification using The Burnout States

Abstract - This paper addresses the classification of ballistic missile using machine learning algorithm, Support Vector Machine (SVM). The objective is to classify the target missiles into one of the predefined classes, in real time, to select the suitable interceptor for Air Defence (AD). The kinematic attributes of the target missiles, Velocity and Specific energy at burnout are used for the classification task. The trajectories corresponding for three types of targets are generated using point mass model. The SVM is selected for the multi-class classification task, trained with these kinematic parameters using supervisory learning method. The trained model is used for the classification task. The accuracy and time required for the target classification plays vital role, for the successful target interception. SVM is able to classify the target with high accuracy in quick time. Keywords - Target Missile Classification, Point Mass Model, Target Data Simulation, Air Defence, Machine learning Algorithm SVM and Real-Time Application.