Micro-Doppler patterns of multi-propeller drones measured by radar systems are widely used in the classification of different drones, since the micro-Doppler patterns illustrate the velocity and motion properties of the drones. However, on this topic, there are a few issues the current researches have not tackled yet, and these will be discussed in this presentation. First of them is the lack of mathematical description of the micro-Doppler patterns. and most research works are based on real measured data so far. In this thesis, an EM backscattering model in HH plane of drone propeller is developed, simplifying the propeller’s geometry structure as a few cylinder thin wires. Radar signal model and micro-Doppler model are subsequently developed for the thin-wire propeller model when it is rotating. Second, most current researches focus on the micro-Doppler patterns achieved in short CPI cases that are valid for radar systems with PRI much shorter than the rotation period of the drone’s propellers. In this thesis, the drone micro-Doppler patterns in long CPI circumstances are investigated. Features are proposed to characterise the amplitude and frequency distribution of the simulated micro-Doppler spectrum. Applying these features to SVM gives good classification accuracy for the simulated micro-Doppler data. Third, most researches at present are carried out in short range for static or stable hovering drones, while from a practical point of view, it is also of great interest to investigate the drone micro-Doppler patterns in long range and dynamic scenarios. In this thesis, the micro-Doppler patterns of different drones at a distance of 9 kilometres are achieved by S-band radar in long CPI circumstance. Applying the previously proposed features to the real measured micro-Doppler spectra to SVM gives good classification accuracy for drones in hovering and manoeuvring flight modes.

This paper proposes to perform radar recognition of multi-propeller drones using micro-Doppler linear spectral pattern in long Doppler coherent processing interval (CPI) circumstances. It focuses on the investigation of the influence of geometry design and motion variables, such as blade number, blade shape, drone’s design geometry and propeller synchronisation, on the micro-Doppler spectral pattern. We propose suitable scalar features for the characterisation of these patterns measured within a long (relatively to propellers rotation period) CPI. A thin-wire model was used to simulate drones micro-Doppler spectra for various input variables. Proposed features are extracted from the simulated micro-Doppler spectra for further processing in a support vector machine (SVM), with the purpose of demonstrating the radar recognition of multi-propeller drones based on these features.