Abstract: An algorithm for flush air data sensing system is designed and improved. A Kalman algorithm is proposed in which iterative computation for the shape and compressibility parameters are avoided. Instead, these parameters are obtained from pressure modification coefficient. A neural network basing algorithm for the modularization is described, and the attack angle and sideslip angle are calibrated by modularization MRCa and MRCb respectively. Methods for getting optimal attack and sideslip angles are developed, solving the multiple solution problem. Calculation and calibration results are presented with improved precision and real-time characteristic, indicating that the design method is feasible.

Key words:

FADS system, Kalman algorithm, triples algorithm, neural network