RCS Estimation of Drone Motion Using Mesh-Element Rotation in MoM and Micro-Doppler Signatures

For an automatic recognition system, a huge number of micro-Doppler signature images is required to train a classifier. However, in general, measurements are only possible for hovering drones, so the dynamic radar cross section (RCS) of a drone with various motions is estimated through EM simulation and converted into a micro-Doppler signature image. In this work, we use far-field approximation and mesh-element rotation in the method of moments to quickly estimate the dynamic RCS of a drone in various motions. First, 3-D meshes and the impedance matrix of a single propeller are generated only once. Instead of rotating the propeller, the position information of the mesh elements is rotated without recreating 3-D meshes or the impedance matrix. Next, using the mirror-image symmetric characteristics of drone propellers and the far-field approximation, the dynamic RCS of multiple propellers is synthesized from that of a single propeller. Finally, the dynamic RCS was estimated at 3 and 9 GHz and converted to micro-Doppler signatures, spectrogram, and cadence-velocity diagram (CVD). As a result, it was more accurate to estimate the dynamic RCS at 9 GHz, and it was easier to obtain the rotation frequency at 3 GHz in the CVD.