Quantification of navigation error associated with the unmanned aircraft system - Ong, Jenny Xue Li (Nanyang Technological University, 2021)
Over the recent years, the use of unmanned aircrafts have been actively explored in various industries, such as last-mile delivery for e-commerce services, air defence and autonomous surveillance operations. With the projected growth of Unmanned Aircraft Systems (UAS) traffic used widely within an urbanized environment, it is important to carry out a safe and efficient drone operation in Singapore. Inaccuracies in GNSS/GPS sensors in flight navigation have been caused by error sources such as multipath effect, atmospheric effects, satellite geometry, etc. Hence, many autonomous drone applications have been implemented through the use of differential correction systems such as Real-Time Kinematics (RTK) to reduce such errors. A high-density populated and low-density populated environment have been used as the flight environments to analyse and quantify the navigation errors associated in UAS. An appropriate analytical model, in the form of machine learning model, is used to provide an accurate indication to the user on the estimated flight accuracy of the environment before they decide on flying the drone for its mission. When comparing the flight accuracy in pre-flight conditions against the flight accuracy after post-flight conditions, the proposed model has a performance accuracy of 89.0% and prediction error of less than 1%.