Real-time wind estimation from the internal sensors of an aircraft using machine learning

A real-time wind velocity vector and parameters estimation and wind model identification approach using a machine learning technique is addressed in this paper. The proposed method uses only the state measurements of an aircraft and does not require control commands, air data systems, or satellite-based data. Small unmanned aerial vehicles (UAVs) can benefit from this method, since it relies solely on measurement results from the common sensors as an attitude and heading reference system. The independence of external sources of information made estimations resistant to intentional errors. This algorithm uses long short-term memory neural networks (LSTM NNs) in a two-step deep learning process involving classification and regression. A classification NN was trained with four different labeled wind models, while individual regression NNs were trained to estimate the velocity vector and parameters of each wind model. The linear acceleration, angular velocity, and Euler angle measurements were used as the inputs of trained networks. The algorithm suggests in its first step identifying the exact wind model, and in its second step estimating the wind velocity vector and parameters using a properly assigned estimation from a trained network. A nonlinear six-degree-of-freedom simulation of straightforward and level turn maneuvers of a fixed-wing UAV in the presence of different wind models served as the dataset in the learning process. Monte Carlo simulations proved the accuracy and rapidity of the proposed algorithm in identifying the wind model and estimating three-dimensional wind velocity vector and parameters.