Embedded UAV model and LASER aiding techniques for inertial navigation systems

Abstract

This work proposes the integration of two aiding information sources to enhance the performance of inertial navigation systems (INS), for precise maneuvering of uninhabited air vehicles (UAVs). A new methodology is derived to exploit vehicle dynamics (VD) information by embeding the VD equations directly in an extended Kalman filter architecture. In this technique, the INS estimates are propagated simultaneously using the VD and the INS differential equations, allowing for the estimation of the INS errors. The proposed technique reduces the computational load associated with classical VD aiding, while retaining the same accuracy enhancements. A LASER range finder sensor is also integrated for precise landing and takeoff maneuvers. Simulation results for the nonlinear dynamics of a Vario X-Treme model-scale helicopter are presented, illustrating the contributions of the proposed aiding techniques for precise UAV navigation.