Airborne Warning System Direction-Finding Based on Real-Time SNR Correction

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.