Helicopter flight maneuver recognition algorithm based on adaptive extended Kalman filter

The flight condition distinguishing is essential for calculation of elapsed time in each regime. The pilots perform different flight regimes during operation which recognize them by combination of flight parameters. Thus, the flight regimes can be defined based on the qualitative descriptions by pilots. Nevertheless, the relation between flight parameters and maneuvers is so complicated and there is no precise mathematic model for flight regime recognition. In this research, a flight regime recognition algorithm is developed based on the qualitative description of maneuvers. A connection matrix is formed using maneuver description to filter the measured flight data and the algorithm identifies the flight regimes. The proposed flight regime recognition algorithm utilized the adaptive extended Kalman filter (AEKF). Using AEKF results in no need for big flight data bank, less sensitivity to initial values and variations, and increases the accuracy during time in contrast with the exiting online regime recognition methods. The algorithm effectiveness is evaluated for the simulated flight data from a validated helicopter dynamic model.