论文标题
基于A-SPC技术
Small Drone Classification with Light CNN and New Micro-Doppler Signature Extraction Method Based on A-SPC Technique
论文作者
论文摘要
随着小型无人机的威胁不仅增加，不仅检测，而且对小型无人机的分类也变得很重要。许多最近的研究采用了一种方法，利用频率调制的连续波（FMCW）雷达利用微型多普勒签名（MDS）进行小型无人机分类。在这封信中，我们提出了一种新的方法，可以用FMCW雷达提取小型无人机的MDS图像。此外，我们提出了一个结构很简单的光卷积神经网络（CNN），并且参数的数量对于快速分类很小。提出的方法通过提高MDS图像的质量来有助于提高分类准确性。我们用常规方法提取的MDS图像和通过拟议的CNN提出的MDS图像对小型无人机进行了分类。实验结果表明，由于提出的方法，总分类精度提高了10.00％。使用拟议的MDS提取方法和拟议的光CNN记录了总分类精度为97.14％。
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.
