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.
Highlights
Without Global Navigation Satellite Systems (GNSS) signals, the navigation system of small unmanned aerial vehicles (UAVs) deteriorates rapidly because of the utilized low-cost microelectromechanical sensor (MEMS)-based Inertial Navigation System (INS)
To aid the navigation system during GNSS signal outages, while accounting for the limitations imposed over these types of drones, this paper presents a fusion between two approaches: (a) the first approach presents an unconventional utilization of contactless rotary magnetic encoder (Hall Effect sensor), which is typically used for RPM measurements, fluid flow rate, ISPRS Int
This paper presented a new approach to enhance the navigation solution during the GNSS signal outage based on Hall effect sensor for velocity estimation “Air-Odo”, and a vehicle dynamic model approach for heading constraint
Summary
Without GNSS signals, the navigation system of small unmanned aerial vehicles (UAVs) deteriorates rapidly because of the utilized low-cost microelectromechanical sensor (MEMS)-based INS. The authors in [22] benefited from the high mobility of the drone to monitor the effect of natural disaster like earthquakes Their navigation system employs two main aiding sensors, LIDAR (Hokuyo 40 Hz), and downfacing camera. To overcome these issues, [31] tried to accommodate different approach by utilizing the VDM as the main process model In this case, if the GNSS/INS measurements are available they serve as an update to the KF. Other unconventional methods/sensors respecting the limitation imposed over these small drones where utilized to aid the navigation system, as in [33], where thermopiles (heat radiation measuring sensor) where used in a different manner to estimate the UAV pitch and roll angle, based on the temperature difference between the ground and the sky. Unlike fixed-wing drones, quadcopters cannot benefit from pitot tube to estimate their velocity, due toUintslikseenfisxiteidv-iwtyintogtdhreodneirse,cqtuioandcoofpthteersaicraflnnoowt,baenndeftihtefrroemqupiriteomt teunbteotfoaessitgimniafitceatnhtecirhvaenlgoeciitny, ddifufeerteontiitaslspernessistuivrietywthoicthhecadninreocttiboenaochf itehveeadirwfiltohws,uacnhddrtohneerseq[3u9i]r.eSmoe, notthoeframseigannsifitcoaenstticmhaantegtehien vdeilfofceirteynotfiatlhperqeussaudrceopwthericihs ccrauncniaoltnboetaocnhliyevtoedenwhiatnhcseutchhe dproosniteiosn[4e0s]t.imSoa,toiotnh,ebr umteaalsnosttooeenshtiamnacteetthhee avtteiltoucdietyesotfimthaetiqounabdeccoaputseer oisf cSrcuhcuilallerneoftfeocntl.yTthoee“nAhiarn-Ocedoth”e, pproessietniotendeisntimthaistipoanp, ebru, tisaalstoecthoneinqhuaentcoe etshtiemaattteittuhdeefoerswtimaradtivoenlobcietcyaoufsetheofquSachducollpetrerefbfaecset.dTohneH“aAllire-fOfedcot ”m, apgrneesteinctseednsionr.this paper, is a technique to estimate the forward velocity of the quadcopter based on Hall effect magnetic sensor
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