In order to solve the problem that the filtering accuracy is reduced due to the time-varying characteristics of wind velocity state system noise and navigation system measurement noise during the calculation of virtual air parameters of space vehicle reentry stage,an adaptive EKF based air parameter estimation method is proposed.Firstly,based on the dynamic equations under the aerodynamic model,the estimation model of wind speed and air parameters is established.Secondly,the Sage-Husa adaptive filtering method is used to adjust the noise of wind speed state system and navigation measurement noise.In order to solve the possible filtering divergence problem,the filtering divergence criterion is introduced and the filtering process parameters are adjusted to improve the filtering stability.Finally,the simulation results show that the proposed adaptive EKF filtering method has better estimation accuracy and convergence stability of wind speed and air parameters,and the estimation accuracy of wind direction,true airspeed and side-slip angle is improved by more than 20%compared with the traditional EKF and Sage-Husa filtering algorithms.
aerospace vehiclevirtual air parameterSage-Husa adaptive filteringextended Kalman filter
NETL
NSTL
维普
万方数据
