Trajectory-based Anomaly Classification of 6-DOF Guided Missile using Neural Networks

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This paper proposes a learning-based anomaly classication of the guided-missile for performance evaluation. Identifying the anomaly of a missile from the performance metrics is difficult while analyzing the detailed parameters from the Monte Carlo simulation requires expertise in the eld. Inspired by recent studies, machine learning is applied to extract features from time-series trajectory data to evaluate the performance of the guided missile. 6-DOF simulation data are used to train the model, and four network structures are tested for the anomaly classication problem. The results show that utilizing the time-series feature from the data enhances the classication accuracy and relational learning reduces the network size signicantly.
Institute of Control, Robotics and Systems (ICROS)
Issue Date

The 9th International Conference on Robot Intelligence Technology and Applications, RiTA2021

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AE-Conference Papers(학술회의논문)
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