Embedded avionics with Kalman state estimation for a novel micro-scale unmanned aerial vehicle

Abstract

An inertial navigation system leveraging Kalman estimation techniques and quaternion dynamics is developed for deployment to a micro-scale unmanned aerial vehicle (UAV). The capabilities, limitations, and requirements of existing navigation solutions motivate the need for an integrated solution that can be readily applied to small embedded systems and still provide reasonably accurate results. Methods to calibrate and compensate systemic inaccuracies in microelectromechanical systems (MEMS) sensors, commonly used in micro-scale UAV applications, are also developed. The problems associated with attitude determination and system localization are analyzed in isolation with incremental simulation and field testing. Performance is evaluated against commercially available inertial navigation system solutions. The result is a capable navigation system that, by its structure, trades a small measure of accuracy in order to be easily adapted to the embedded computing constraints of unmanned vehicles in the micro-scale.