Print Email Facebook Twitter Radar Micro-Doppler Patterns for Drone's Characterisation Title Radar Micro-Doppler Patterns for Drone's Characterisation Author Cai, Yefeng (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Yarovyi, Olexander (mentor) Krasnov, Oleg (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2019-05-29 Abstract 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. Subject radarmicro-Dopplerdronesimulationlong CPI To reference this document use: http://resolver.tudelft.nl/uuid:2fe8fa3f-b4f9-45ea-a8c3-912aa433ba87 Part of collection Student theses Document type master thesis Rights © 2019 Yefeng Cai Files PDF YefengCai_MscThesis.pdf 23.21 MB Close viewer /islandora/object/uuid:2fe8fa3f-b4f9-45ea-a8c3-912aa433ba87/datastream/OBJ/view