Air Data Estimation Based on Adaptive Kalman Filter

Abstract

Air data including true airspeed, angle of attack and sideslip angle are important flight parameters. Under atmospheric disturbance, it is difficult to measure air data accurately in flight. A kind of air data estimation method based on flight data is studied. According to the flight dynamics model and aircraft modeling data, the system state equation and measuring equation were built up respectively. In consideration of the different sampling rates and uncertain covariance of sensor noise, an information adaptive Kalman filter with non-equally spaced time series method was adopted to estimate the flight states. By turning flight simulation, the results show that the angle of attack estimation error is less than 0.1°, while the estimation error of sideslip angle is less than 0.05°. Compared with standard Extended Kalman Filter, information adaptive Kalman filter can track the flight states and estimate real air data more accurately.