Ship-Mounted Radio Guidance System for Fixed-Wing Drone Recovery

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.

Popular Abstract

Catching Drones at Sea: A Smart Radio Compass for Rescue Boats
Imagine being lost at sea, where every minute matters. This thesis shows how
drones used in search and rescue can be accurately recovered on moving rescue
vessels despite constant wave motion. The developed system achieved tracking er-
rors of around one meter, providing a promising foundation for future autonomous
shipboard drone recovery.
For organizations such as the Swedish Sea Rescue Society (SSRS), long-range
drones could significantly improve search and rescue operations by covering large
areas of ocean much faster than rescue vessels. However, these drones currently
need to return to shore to land, limiting the time they can spend searching for
people in distress. This thesis investigates a key technology needed to solve that
problem: reliable navigation between a drone and a moving vessel. Recovering a
drone on a boat is far more difficult than landing on a runway because the vessel
is continuously pitching, rolling, and moving with the waves. These motions can
distort the measurements used to guide the drone. To overcome this challenge, a
ship-mounted radio guidance system was developed that functions as a smart ra-
dio compass. Satellite navigation guides the drone toward the vessel, while short-
range radio measurements provide accurate tracking during the final approach.
Motion sensors on the vessel continuously measure its movement, allowing the
system to compensate for wave-induced motion and maintain a stable estimate
of the drone’s position. The system was evaluated through computer simulations
and physical experiments under dynamic conditions. The results showed that the
motion-compensation approach successfully separated the drone’s true direction
from disturbances caused by vessel movement, maintaining stable tracking per-
formance throughout the wave-motion experiments. Although fully autonomous
recovery could not be demonstrated due to drone hardware limitations, the results
show that accurate relative navigation between a drone and a moving vessel is
achievable. In the future, such technology could allow rescue drones to remain
airborne longer, search larger areas, and help emergency services reach people in
need quicker.

• author: Forrez, Beau
• supervisor: Anders J Johansson
• organization: Department of Electrical and Information Technology
• course: EITM01 20261
• year: 2026
• type: H1 - Master's Degree (One Year)
• subject: Technology and Engineering
• keywords: Search and Rescue, Unmanned Aerial Vehicles, UAV, Drones, Maritime Operations, Autonomous Recovery, Autonomous Landing, Sensor Fusion, Extended Kalman Filter, EKF, Bluetooth Angle of Arrival, AoA, Global Navigation Satellite System, GNSS, Terminal Guidance, Motion Compensation
• report number: LU/LTH-EIT 2026-1180
• language: English
• id: 9243220
• date added to LUP: 2026-06-24 15:59:31
• date last changed: 2026-06-24 15:59:31