@misc{9243220,
  abstract     = {{The Swedish Sea Rescue Society’s (SSRS) maritime Search and Rescue (SAR) op-
erations can be enhanced through the use of fixed-wing unmanned aerial vehicles
(UAVs), which offer extended range and rapid area coverage. However, recovering
such aircraft aboard small, moving rescue vessels remains a significant challenge
due to continuous wave-induced motion (pitch, roll, and yaw) and the absence of
conventional runways. This thesis investigates the development of a ship-mounted
radio guidance system to support the autonomous recovery of fixed-wing UAVs in
maritime environments. The proposed system employs a two-stage sensor-fusion
architecture that combines differential Global Navigation Satellite System (GNSS)
positioning for long-range guidance with Bluetooth 5.1 Angle of Arrival (AoA)
measurements for terminal homing. To compensate for vessel motion, inertial mea-
surement unit (IMU) data is used to transform raw AoA measurements into an
Earth-fixed North-East-Down (NED) reference frame. The resulting asynchronous
sensor streams are fused in real time using an Extended Kalman Filter (EKF) to
estimate the relative position of the approaching UAV. The system was evaluated
through Software-in-the-Loop (SITL) simulations and human-in-the-loop field ex-
periments. Results demonstrate that the proposed coordinate transformation and
sensor-fusion framework effectively separates the true target approach vector from
vessel-induced disturbances, maintaining tracking errors of approximately one me-
ter. Although hardware limitations prevented full autonomous flight validation,
the developed system demonstrates the feasibility of robust, low-latency relative
navigation for maritime UAV recovery. The presented architecture provides a
foundation for future autonomous fixed-wing drone recovery systems in maritime
SAR operations.}},
  author       = {{Forrez, Beau}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Ship-Mounted Radio Guidance System for Fixed-Wing Drone Recovery}},
  year         = {{2026}},
}