Purpose The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics.Designmethodologyapproach The introduced adaptive filter scheme is composed of two parallel UKFs. At every timestep, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UKF estimates the noise covariance using the innovations generated by the master UKF. This real time innovationbased adaptive unscented Kalman filter UKF is used to estimate aerodynamic parameters of aircraft in uncertain environment where noise characteristics are drastically changing.Findings The investigations are initially made on simulated flight data with moderate to high level of process noise and it is shown that all the aerodynamic parameter estimates are accurate. Results are analyzed based on Monte Carlo simulation with 4000 realizations. The efficacy of adaptive UKF in comparison with the other standard Kalman filters on the estimation of accurate flight stability and control derivatives from flight test data in the presence of noise, are also evaluated. It is found that adaptive UKF successfully attains better aerodynamic parameter estimation under the same condition of process noise intensity changes.Research limitationsimplications The presence of process noise complicates parameter estimation severely. Since the nonmeasurable process noise makes the system stochastic, consequently, it requires a suitable state estimator to propagate the states for online estimation of aircraft aerodynamic parameters from flight data.Originalityvalue This is the first paper highlighting the process noise intensity change on real time estimation of flight stability and control parameters using adaptive unscented Kalman filter.
Aircraft Engineering and Aerospace Technology – Emerald Publishing
Published: Jun 28, 2013
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