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In this study, we aimed to predict the failure of a system that includes multiple anti-drone systems, namely radar and jammer devices. It is anticipated that predictive maintenance, which has been attempted in many areas before, in the anti-drone field will reduce the costs. In this study, we developed a feature selection that suits the requirements of the problem. Afterward, Support Vector Machines (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) models are adapted to our problem. Created models are fed with one hand-crafted single device data set, one synthetic single device data set, and one synthetic multi-device data set and reported the success rates. It is observed that the results provide successful predictions that could used in the field.

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Failure Prediction for Large Anti-drone System Clusters

  • 会议论文

  • 发表日期：

    2025年

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  • Buket Kazma,
  • Fatih Semiz

摘要

In this study, we aimed to predict the failure of a system that includes multiple anti-drone systems, namely radar and jammer devices. It is anticipated that predictive maintenance, which has been attempted in many areas before, in the anti-drone field will reduce the costs. In this study, we developed a feature selection that suits the requirements of the problem. Afterward, Support Vector Machines (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) models are adapted to our problem. Created models are fed with one hand-crafted single device data set, one synthetic single device data set, and one synthetic multi-device data set and reported the success rates. It is observed that the results provide successful predictions that could used in the field.