A Study on Detection of Small UAV Based on Micro-Doppler Signatures

Abstract

Detection of small unmanned aerial vehicles (UAVs) is challenging problem, because of their small radar cross-section (RCS) and low-altitude and low-speed flight. The micro-Doppler (MD) signature induced by a flying UAV can provide unique characteristics related to the kinetics of a small UAV. Therefore, the MD signature can be utilized for detection of a small UAV. In this dissertation, we discuss efficient UAV detection chains based on the MD signature. First, the MD signatures caused by small UAVs are analyzed. For this, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum. Both simulation and experimental analyses are conducted considering types of small UAVs, the translational motion, and aspect change. As a result, we completely explain the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the joint time-frequency (JTF) images, is a considerably simple and useful tool for analyzing and detecting the MD effects of small flying UAVs. Based on the analysis of MD signatures of small UAVs, we propose an efficient scheme to detect small UAVs using both the range profile and the Doppler spectrum. The proposed detection chain is performed in three steps: 1) preprocessing with clutter rejection and noncoherent integration 2) coarse detection using the range profile 3) fine detection using the Doppler spectrum. The main contribution of this study is that the fine detection, free from learning with training data, can detect the MD signatures of flying UAVs with high accuracy under dynamic clutter conditions. The coarse detection improves detection capability compared to conventional methods based on the range profile. Additionally, the proposed processing sequence reduces the additional cost required for detecting the MD signature. Consequently, the proposed scheme based on the MD signature guarantees both robust detection performance and efficiency even in the presence of dynamic clutter.