Most existing work on radar classification of micro-drones assumes that the received signal is wholly reflected from a single micro-drone. However, when there are multiple micro-drones in the observed scene, the superimposition of their micro-Doppler signatures increases the classification difficulty. In particular, it is even more challenging to determine if a specific type of micro-drones exists. In this study, a method for recognition of multiple micro-drones based on their micro-Doppler signatures via dictionary learning is introduced. First, the dictionary is learnt for each type of micro-drone by using the K-SVD algorithm on cadence-velocity diagrams (CVD) of training samples. The CVD is obtained by computing the Fourier transform of the time series of a complex time–frequency spectrogram. Subsequently, the sparse representation of the CVD of multiple micro-drones is obtained by the orthogonal matching pursuit algorithm with the learnt dictionary. Finally, a threshold detector is applied to the sparse solution in order to extract the components of multiple micro-drones. Experimental results using measured data, which are collected from hovering drones by a continuous-wave radar in an indoor environment, show that this dictionary-learning-based method achieves a recognition performance of 93% when half of the measured data are used for training.

中文翻译：

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通过字典学习，基于微型多普勒签名对多个微型无人机进行雷达识别

现有的有关微型无人机雷达分类的大多数工作都假设接收到的信号是完全由单个微型无人机反射的。但是，当观察到的场景中有多个微型无人机时，它们的微型多普勒签名的叠加会增加分类的难度。特别地，确定是否存在特定类型的微型无人机甚至更具挑战性。在这项研究中，介绍了一种基于字典学习的基于微多普勒签名的多微无人机识别方法。首先，通过在训练样本的步速-速度图（CVD）上使用K-SVD算法为每种类型的微型无人机学习字典。通过计算复杂时频频谱图的时间序列的傅立叶变换获得CVD。后来，通过带有学习字典的正交匹配追踪算法，获得了多个微型无人机CVD的稀疏表示。最后，将阈值检测器应用于稀疏溶液，以提取多个微型无人机的组件。使用在室内环境中由连续波雷达从悬停无人机捕获的测量数据进行的实验结果表明，当一半的测量数据用于训练时，这种基于字典学习的方法可实现93％的识别性能。