Data Selection for Training the Neural Fuser Applied to Autonomous UAV Navigation
Copyright policy )Data Selection for Training the Neural Fuser Applied to Autonomous UAV Navigation
Over the past few years, there has been a steady increase in the use of aircraft vehicles, in particular unmanned aerial vehicles (UAV). UAV navigation is generally controlled by a human pilot. But the challenge for the scientific community is to carry out autonomous navigation. Some solutions have been proposed for the UAV autonomous navigation. Studies indicate as a solution to use data fusion and/or image processing navigation. Kalman Filter (KF) can be employed as a data fuser, but the KF has disadvantages. An alternative to the KF is based on artificial intelligence. Here, the KF is replaced by a self-configured neural network. This work investigates a way to select data for training the neural fuser, based on crossvalidation techniques. The results are compared to the data fusion done by a KF.
self-configured neural network, Artificial neural network, Artificial intelligence, Particle Filtering and Nonlinear Estimation Methods, Kalman Filters, Aerospace Engineering, FOS: Mechanical engineering, Simultaneous Localization and Mapping, cross-validation, Real-time computing, Engineering, Meteorology, Artificial Intelligence, Training (meteorology), unmanned aerial vehicle, QA1-939, Navigation system, Sensor fusion, Geography, Control engineering, Cross-validation, Unmanned aerial vehicle, Kalman Filtering, Computer science, k-fold, Self-configured neural network, Computer Science, Physical Sciences, Computer vision, Inertial Navigation Systems and Sensor Fusion Techniques, Kalman filter, Mathematics
self-configured neural network, Artificial neural network, Artificial intelligence, Particle Filtering and Nonlinear Estimation Methods, Kalman Filters, Aerospace Engineering, FOS: Mechanical engineering, Simultaneous Localization and Mapping, cross-validation, Real-time computing, Engineering, Meteorology, Artificial Intelligence, Training (meteorology), unmanned aerial vehicle, QA1-939, Navigation system, Sensor fusion, Geography, Control engineering, Cross-validation, Unmanned aerial vehicle, Kalman Filtering, Computer science, k-fold, Self-configured neural network, Computer Science, Physical Sciences, Computer vision, Inertial Navigation Systems and Sensor Fusion Techniques, Kalman filter, Mathematics
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