Enhanced Drone Navigation in GNSS Denied Environment Using VDM and Hall Effect Sensor

Abstract

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.

Waypoint and autonomous flying control of an indoor drone for GPS-denied environments

“…Using ( 8), MATLAB was again used to test the performance of estimating the 3D location. The true location was set as (2,7,3), where all units are meters. Figure 5 shows the result of position estimation by testing 100 times.…”

Review of Intentional Electromagnetic Interference on UAV Sensor Modules and Experimental Study

“…In the era of the 4th Industrial Revolution, drones have achieved innovative performance improvements based on new technologies, such as machine intelligence, networks, and sensors [ 1 , 2 , 3 , 4 , 5 , 6 ]. With the development of these technologies, drones can be operated reliably even in rapidly changing external environments; as a result, they are being used to perform various tasks that are difficult or avoided by humans, such as agriculture, disaster management, and fire detection [ 7 , 8 , 9 , 10 , 11 , 12 ].…”

Backstepping Control of Drone

“…Hence, a detailed drone description model is described in [2][3][4]. The sliding mode control strategy based on backstepping control is widely used as it can produce high performances and a faster response for drone systems in general [5][6][7][8][9] and in indoor micro-quadrotors in particular [10]. Modeling-and backstepping-based nonlinear control for a six-DOF quadrotor helicopter is proposed by [11], in addition to the proposition of an adaptive sliding mode control for a quadrotor helicopter in [12].…”