Abstract
This paper proposes a learning-based anomaly classification 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 field. 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 classification problem. The results show that utilizing the time-series feature from the data enhances the classification accuracy and relational learning reduces the network size significantly.
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Acknowledgements
This research was supported by the Agency for Defense Development and Defense Acquisition Program Administration under the Intelligence Flight Control Research Project (Contract Number: UD200045CD).
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Park, SJ., Oh, Y., Kim, S.J., Kim, YW., Choi, HL., Lee, CH. (2022). Trajectory-Based Anomaly Classification of 6-DOF Guided Missile Using Neural Networks. In: Kim, J., et al. Robot Intelligence Technology and Applications 6. RiTA 2021. Lecture Notes in Networks and Systems, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-030-97672-9_43
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DOI: https://doi.org/10.1007/978-3-030-97672-9_43
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