Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

Most research on drone detection and classification focus on using features from micro-Doppler signatures with blade flashes. However, these methods are limited in range and require radars with high pulse repetition frequency (PRF)-at least twice the maximum tip velocity. A different method to detect and classify drones at longer ranges using a low PRF radar is desired. In the literature, the cepstrum method was shown to be able to estimate the rotation rate when the PRF is insufficient. An alternative way of analyzing micro-Doppler is by using a long windowed Short-time Fourier transform (STFT) to generate HElicopter Rotation Modulation (HERM) lines. HERM lines exhibit similar behavior to a cepstrogram, with spectral lines separated in frequency by a value related to the rotation rate. In this paper, the separation frequency of HERM lines was estimated using a log harmonic summation algorithm. The proposed algorithm was tested on a simple HERM line model and also on real data obtained from two blade single rotor micro-helicopter drone. The algorithm was shown to be more resilient than cepstrum under Gaussian noise.