Experimental evaluation demonstrates improved direction-finding accuracy in airborne warning systems, highlighting robust counter-drone tracking under low signal-to-noise conditions.
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.
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Ding et al. (2026) studied this question.
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