Hybrid Machine Learning VDM for UAVs in GNSS-denied Environment

Abstract

This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) autonomous navigation, without adding extra load to the vehicle. The proposed approach employs the UAV vehicle dynamic model to aid the navigation estimation in a Global Navigation Satellite Systems (GNSS)-denied environment, without the need to model any part of the UAV, and avoids the requirement for special equipment during the modeling procedures typically required for vehicle dynamic model-aided navigation. Taking advantage of the available information from previous flights during availability of GNSS, and with the aid of a hybrid machine learning approach, the proposed technique is able to enhance the navigation accuracy during GNSS outage, despite the massive drift occurring from utilizing a low-cost inertial measurement unit (IMU) during the outage period. Different scenarios are investigated to prove the robustness of the proposed technique, and the results are compared to two types of IMUs with the aid of an inverse mechanization IMU simulator