A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the flight test instrumentation (FTI) system components becomes crucial. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. A patented virtual sensor, based on artificial neural network (ANN) techniques, named smart-air data, attitude and heading reference system (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. As far as unmanned aerial vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. Several architectures exist to measure aerodynamic angles based on physical sensors. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data. The present work’s objective is to evaluate performances of a single-layer feed-forward generalized radial basis function network for AoA estimation trained with a sequential algorithm. An alternative is offered by regularization networks, such as radial basis function, to cope with training domain based on real flight data. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse, and unbalanced training domain. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. ![]() The angle of attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g., model-based, data-driven, and model-free state observers. ![]() Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. Synthetic sensors enable flight data estimation without devoted physical sensors.
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