Improved UAS Distance Estimation via LiDAR-Radar Data Fusion Using Extended Kalman Filter

Unmanned Aircraft Systems (UAS) represent a novel and promising alternative for air transportation, with applications spanning civil, scientific, and military domains. Due to their versatility, flexibility, and reusability, UAS are deployed across a variety of operational scenarios, including search and rescue, disaster assessment, urban traffic monitoring, power line inspection, agricultural crop monitoring and spraying, as well as in environments that pose significant risks or are inaccessible to human intervention. The adoption of UAS is steadily increasing, for multiple applications, including the ones in urban areas, where stringent operational safety standards are required. Therefore, to ensure the safety of autonomous UAS flight operations, the implementation of Detect and Avoid (DAA) systems is of paramount importance, in order to detect other aircraft or obstacles and to identify safe trajectories within a given mission scenario. This paper focuses on a data fusion approach for distance estimation using a 3D rotary LiDAR sensor and an on-chip Radar module. The proposed data fusion methodology employs the Extended Kalman Filter (EKF) to enhance sensor data integration and accuracy. The utilization of these sensors is critical for obstacle detection systems in UAS operations, particularly during autonomous or Beyond Visual Line of Sight (BVLOS) missions.