Radar recognition of multiple micro‐drones based on their micro‐Doppler signatures via dictionary learning

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.