Abstract
This paper proposes a new algorithm for the aerodynamic parameter and noise estimation for aircraft dynamical systems. The Bayesian inference method is combined with an unscented Kalman filter to estimate the augmented states and the unknown noise covariance parameters jointly. A Gauss‐Newton method is utilized to sequentially maximize the posterior likelihood function for the noise unknown parameter estimation. The performance of the proposed algorithm is evaluated and compared with two other UKFs via a flight scenario of a given aircraft. The results indicate that the proposed algorithm has equivalent performance to the simplified UKF with prior noise information and slightly outperforms the parallel UKF on precision and efficiency in this flight scenario assessment. Then, the consistency and accuracy of the algorithm are further validated by a Monte Carlo simulation with random process noise covariance. This adaptive algorithm provides another feasible and effective way for estimating aerodynamic parameters from the aircraft real flight data with unknown noise characteristics.
Highlights
The environment disturbance and sensor noise are the primary contamination sources to aircraft flight data
The generic nonlinear system can be represented by a model with additive Gaussian noise: CLα which is proposed in this paper, the simplified UKF (SUKF) with prior noise knowledge, and the parallel UKF (PUKF) proposed in reference [5]: (a) estimation results of C Lα ; (b) estimation results of C Mα ; (c) estimation results of C Mq ; (d) estimation results of CMδe
By combining the above methods, we present a Bayesian adaptive unscented Kalman filter to handle the aircraft aerodynamic parameter estimation problem with the unknown noise characteristics
Summary
State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Centre, Mianyang 621000, China. Computational Aerodynamics Institute, China Aerodynamics Research and Development Centre, Mianyang 621000, China. Aerospace Technology Institute, China Aerodynamics Research and Development Centre, Mianyang 621000, China. This paper proposes a new algorithm for the aerodynamic parameter and noise estimation for aircraft dynamical systems. The performance of the proposed algorithm is evaluated and compared with two other UKFs via a flight scenario of a given aircraft. The consistency and accuracy of the algorithm are further validated by a Monte Carlo simulation with random process noise covariance. This adaptive algorithm provides another feasible and effective way for estimating aerodynamic parameters from the aircraft real flight data with unknown noise characteristics
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