  As the threats of small drones increase, not only the detection but also the
classification of small drones has become important. Many recent studies have
applied an approach to utilize the micro-Doppler signature (MDS) for the small
drone classification by using frequency modulated continuous wave (FMCW)
radars. In this letter, we propose a novel method to extract the MDS images of
the small drones with the FMCW radar. Moreover, we propose a light
convolutional neural network (CNN) whose structure is straightforward, and the
number of parameters is quite small for fast classification. The proposed
method contributes to increasing the classification accuracy by improving the
quality of MDS images. We classified the small drones with the MDS images
extracted by the conventional method and the proposed method through the
proposed CNN. The experimental results showed that the total classification
accuracy was increased by 10.00 % due to the proposed method. The total
classification accuracy was recorded at 97.14 % with the proposed MDS
extraction method and the proposed light CNN.
