EXTENDED KALMAN FILTER BASED AIRBORNE SIMULTANEOUS LOCALIZATION AND MAPPING
UAVs are expected to perform all or part of its mission as autonomous in accordance with predefined at the manufacture objectives and prerequest of safe autonomous navigation is to detect the UAV location precisely. Nowadays the most common method for location detection is the use of Global Navigation Satellite Systems (GNSS). Howewer but it is a challenge for researchers to determine location in GNSS denied enviroments. Although new methods are emerging continuously, the most notably one is Simultaneous Localization and Mapping (SLAM), which is a good solution when both UAVs position and region map are not known.
In this research, formulas of Extended Kalman Filter (EKF) based (A-SLAM) Airborne Simultaneous Localization and Mapping, filter for UAVs when GNSS denied and map is not known are put forward and EKF based A-SLAM simulation is performed by MATLAB Simulink and for this purpose, the UAV kinematic model is obtained and both state and observation models are constructed, simulation results of EKF-based A-SLAM in accordance with a certain scenario are yielded.
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