Neural Networks for Air Data Estimation: Test of Neural Network Simulating Real Flight Instruments

In this paper virtual air data sensors have been modeled using neural networks in order to estimate the aircraft angles of attack and sideslip. These virtual sensors have been designed and tested using the aircraft mathematical model of the De Havilland DHC-2. The aim of the work is to evaluate the degradation of neural network performance, which is supposed to occur when real flight instruments are used instead of simulated ones. The external environment has been simulated, and special attention has been devoted to electronic noise that affects each input signals examining modern devices.. Neural networks, trained with noise free signals, demonstrate satisfactory agreement between theoretical and estimated angles of attack and sideslip.