Enhancement of Drone ISAR Imaging Through Isolation and Suppression of Micro-Doppler Effects

Increasing concerns over the use of drones for military or hostile purposes have intensified the demand for effective detection and countermeasures. Although conventional acoustic, thermal, and optical sensors have been extensively explored, active radar systems are particularly promising due to their allweather capability and simultaneous range-velocity measurements. However, the low radar cross-section (RCS) of small drones remains a critical obstacle for reliable detection and identification. Although recent advancements in ultra-wideband (UWB) and high-power frequency-modulated continuous wave (FMCW) radars have improved sensitivity, they often fail to distinguish drones from other aerial objects, leading to false alarms and classification errors. A primary source of these errors is the high-frequency micro-Doppler signature generated by the rapidly rotating propellers, which corrupts the phase information required for high-resolution imaging. To address this problem, we propose a method to isolate and suppress these micro-Doppler artifacts, thereby enabling robust phase compensation for inverse synthetic aperture radar (ISAR) processing. The proposed processing framework integrates three key stages: (A) a Chebyshev polynomial-based filter to suppress propeller-induced spectral components, (B) spectral-domain averaging to enhance the signal from the drone body, and (C) the phase gradient autofocus (PGA) algorithm to correct the residual phase errors. The proposed methodology was analyzed theoretically using simplified target models and subsequently validated using experimental data obtained from real-world drone flights. By improving the clarity and contrast of drone ISAR images, the proposed method provides a practical processing framework for mitigating micro-Doppler-induced degradation in maneuvering drone scenarios.