Radar Moving Target Classification Via CNN
Radar-based classification of ground moving targetsrelies on Doppler information. Therefore, the classification be-tween humans and animals is a challenging task due to theirsimilar Doppler signatures. This work proposes a Deep Learning-based approach for ground moving radar targets classification.The proposed algorithm learns the radar targets’ micro-Dopplersignatures in the 2D fast-time slow-time domain of radar echoes.This work shows that the convolutional neural network (CNN)can achieve high classification performance. Also, it shows thatefficient data augmentation and regularization significantly im-prove classification performance and reduce over-fit.
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Radar-Moving-Target-Classification-Via-CNN
Radar-based classification of ground moving targetsrelies on Doppler information. Therefore, the classification be-tween humans and animals is a challenging task due to theirsimilar Doppler signatures. This work proposes a Deep Learning-based approach for ground moving radar targets classification.The proposed algorithm learns the radar targets’ micro-Dopplersignatures in the 2D fast-time slow-time domain of radar echoes.This work shows that the convolutional neural network (CNN)can achieve high classification performance. Also, it shows thatefficient data augmentation and regularization significantly im-prove classification performance and reduce over-fit.
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Audited on May 14, 2025