Armored Target Threat Assessment Method Based on Dynamic Graph Convolutional Neural Network
A threat assessment method for armored targets based on dynamic graph convolutional neural network is proposed to address the issues of large subjective factors,isolated consideration of target features,and neglect of timing in traditional threat assessment methods.This paper analyzes the operational issues of armored equipment on land battlefields,selects reasonable target features to preprocess them,establishes an initial threat assessment graph based on the selected target features,uses dynamic graph convolutional neural networks to perform deep learning on the established initial graph,and conducts a simulation using a numerical example.The evaluation results compared with traditional methods indicate that threat assessment based on dynamic graph convolu-tional neural networks has high accuracy and strong robustness,and is more suitable for practical land battlefield environments with high dynamic and strong correlation.
threat assessmentarmored on land battlefielddynamic graph convolutional neural network
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