De Witt, Josias JNel, Willem A2026-01-152026-01-152025-10979-8-3315-4433-1DOI: 10.1109/RadarConf2559087.2025.11204965http://hdl.handle.net/10204/14596Low-flying helicopters are vulnerable to small arms fire, highlighting the need for Hostile Fire Indication (HFI) systems that can detect bullet threats and estimate shooter bearing to support evasive action or counter measures. Acoustic sensors have traditionally been used for shot detection but are limited in airborne environments due to noise, atmospheric variability, and weather sensitivity. Radar offers an attractive, all-weather alternative, but conventional angle-of-arrival (AoA) estimation requires complex, multi-channel sensors. This paper proposes a novel alternative approach using only low-complexity, single-channel FMCW radar sensors mounted around the airborne platform. Although these sensors cannot measure AoA individually, combining their bullet detection data with machine learning enables shooter bearing estimation. A Random Forest regressor, trained on simulated bullet trajectories from AK47 and AR15 rifles, achieves shooter bearing estimation RMSE below 4° for miss distances under 20 m, with useful accuracy (RMSE<15°) up to 40 m miss distance. The approach enables a low-complexity, cost-effective, radar-based HFI solution for airborne platforms.AbstractenHostile Fire IndicationHFIShooter bearing estimationBullet detectionMachine learningConference Presentationn/a