E. Chang, R. Sturdivant, Bryce S. Quilici, Erich W. Patigler
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Micro and mini drone classification based on coherent radar imaging
Low, slow, and small unmanned aerial system (LSS-UAS) are a growing threat to the civil and military sectors. This paper reports the results of a feasibility study on a radar-based drone feature-classification system. A 9.6 GHz CW radar was used to acquire Doppler data. Coherent integration of the data in conjunction with motion extraction and compensation turned this conventional Doppler radar into a one-dimensional inverse synthetic aperture radar (ISAR), which produced the detailed image of a mini drone. This result paves the way for a fully intelligent classification system with multiple sensors for countering the LSS-UAS threat.
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