Drone Characterization using Helicopter Rotation Modulation (HERM) Line Signatures
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This thesis presents research on the radar micro-Doppler drone characterization using features from the long windowed short-time Fourier transform (STFT) representation referred to as HElicopter Rotation Modulation (HERM) line signatures. The long windowed STFT representation enables drone identification at longer ranges using a low pulse repetition frequency (PRF) radars. A simple mathematical model is proposed to describe the HERM line signature using three parameters or features, namely the number of HERM lines, the fundamental frequencies and the number of fundamental frequencies. This model motivates research into parametric spectral estimation techniques for micro-Doppler feature extraction which is lacking in the current state of the art. Subspace-based spectral estimation techniques are applied to real and simulated data and compared with the Fourier-based techniques from the literature. These proposed techniques are shown to perform better for smaller windows of data. An application of these features for drone detection/classification is demonstrated.
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Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2021
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huang-dronecharacterizationusinghelicopterrotation.pdf | 2023-05-05 | Public | Download |