Resumen
The last decade has witnessed a wide spread of small drones in many civil and military applications. With the massive advancement in the manufacture of small and lightweight Inertial Navigation System (INS), navigation in challenging environments became feasible. Navigation of these small drones mainly depends on the integration of Global Navigation Satellite Systems (GNSS) and INS. However, the navigation performance of these small drones deteriorates quickly when the GNSS signals are lost, due to accumulated errors of the low-cost INS that is typically used in these drones. During GNSS signal outages, another aiding sensor is required to bound the drift exhibited by the INS. Before adding any additional sensor on-board the drones, there are some limitations that must be taken into considerations. These limitations include limited availability of power, space, weight, and size. This paper presents a novel unconventional method, to enhance the navigation of autonomous drones in GNSS denied environment, through a new utilization of hall effect sensor to act as flying odometer ?Air-Odo? and vehicle dynamic model (VDM) for heading estimation. The proposed approach enhances the navigational solution by estimating the unmanned aerial vehicle (UAV) velocity, and heading and fusing these measurements in the Extended Kalman Filter (EKF) of the integrated system.