AIAA Guidance, Navigation, and Control Conference and Exhibit · 2000 · 11 citations · 5 references
ComputingAerospace EngineeringAutonomous SystemsAutonomous VehiclesSensor FusionUnmanned Aircraft SystemsAdaptive ControlKalman FilteringMissile Flight DynamicsNavigation SystemsSensor Fusion For LocalizationTrajectory PredictionAdaptive Kalman FilterExtended Kalman FilterExtended Kalman FilteringGlobal Positioning SystemInertial NavigationInertial Navigation SystemsPositionUav Navigation
This paper presents an approach to deal with the accuracy of an Extended Kalman Filter (EKF) by using a moreaccurate continuous model, but increasing EKF's speed with parallelization. Previous work, which was based on sacricing a continuous state model in favor of discrete equations with Fuzzy Adaptive Kalman Filter led to some interesting results did not oer sucient accuracy improvement. Two case studies arepresented: the fuzzy lter applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles, and the parallel lter validated for referencetrajectories of the ballistic missile with the addition of equations converting the planar equations of motion to three dimensional equations. Introduction In navigation of an autonomous vehicle, suchas an Unmanned Aerial Vehicle (UAV), one needs to plan the path dependence on the known information, determine the position and velocity of the vehicle, and avoid ...
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