Jia Ding, Huaizong Shao, Haiwei Song, Jiawei Zhang, Fake Ding, Wen Zhang, Jie Liu, Jianxing Lv
The rapid advancement of unmanned aerial vehicle (UAV) technology has introduced increasingly severe airspace security challenges. As a critical component of counter-UAV systems, the direction-finding (DF) accuracy of airborne warning systems directly affects threat assessment and response efficiency. This paper addresses the problem of limited DF accuracy in airborne environments by proposing a direction-finding method that integrates attitude self-calibration with real-time signal-to-noise ratio (SNR) estimation. Based on the monopulse amplitude-phase comparison angle-measurement principle, the proposed method dynamically corrects the angle-discrimination curve using real-time SNR information and adaptively calibrates azimuth information by incorporating UAV attitude data. Simulation and experimental results demonstrate that the proposed method significantly reduces angle-measurement errors under low-SNR conditions, and attitude calibration further improves DF accuracy across the full angular range. Field experiments indicate that, after attitude calibration, the angle-measurement error is less than 2∘ in over 77% of the test points.