无人机微多普勒分类的时间深度学习

Daniel A. Brooks, Olivier Schwander, F. Barbaresco, J. Schneider, M. Cord
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引用次数: 40

摘要

这项工作建立了时间深度学习架构,用于在模拟雷达数据集的新模型上对时频信号表示进行分类。我们展示并比较了这些模型的成功,并验证了时间结构对随着时间的推移获得分类置信度的兴趣。
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Temporal Deep Learning for Drone Micro-Doppler Classification
This work builds temporal deep learning architectures for the classification of time-frequency signal representations on a novel model of simulated radar datasets. We show and compare the success of these models and validate the interest of temporal structures to gain on classification confidence over time.
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