An open benchmark dataset for machine learning and intelligent trajectory optimization in fixed-wing unmanned aerial systems

This paper presents an open-access telemetry dataset designed to support research and training in intelligent fixed-wing unmanned aerial systems. The dataset contains 240 fully annotated autonomous missions flown outdoors over repeatable, waypoint-based trajectories using two onboard architectures: a compact SpeedyBee F405 flight controller running INAV, and a Holybro Pixhawk 6X paired with a Jetson Orin NX companion computer running PX4. The missions cover key phases, including take-off, cruise, dynamic manoeuvres, and autonomous landing. Each log provides synchronised multi-sensor telemetry (IMU, GNSS, barometric altitude, actuator states, flight modes, and power metrics) at high temporal resolution, enabling realistic modelling of flight dynamics, estimator behaviour, and sensor noise. The dataset supports benchmarking for trajectory tracking under degraded GNSS, anomaly detection, wind-aware navigation, and energy-optimised mission planning. The paper documents hardware integration, communication architecture, mission procedures, and the dataset file structure, and includes representative analyses to illustrate reuse for contested, safety-critical, and complex operational environments in field. No neural network is trained or evaluated; deep learning is cited only as a motivating application domain.