Abstract:
Due to the correlation between flight and ground test data, there is a certain difference between the wind tunnel test data and the flight test data. Limited by the flight trajectory and test excitation, the flight test data distributes in a small characteristic sections of the flight trajectory. And due to the limitation of ground simulation capabilities, wind tunnel tests have certain differences between the test data and the actual flight data. In order to remedy these limitations, researches of the data fusion algorithm based on above two different sources of aerodynamic data are conducted. Firstly, through the aerodynamic parameter identification method, the six-component real aerodynamic data of the aircraft along the actual flight trajectory are obtained, and then compare the difference and consistency with the wind tunnel test data. Finally, two kinds of data fusion methods based on gradient information and Gaussian process regression were used to fuse the two sources of aerodynamic datas. The results show that both the prediction data of the two fusion models are more accurate than the single source model; if the gradient information of the high and low precision data is more consistent, the data fusion method based on the gradient information is more effective; while the data fusion method based on the Gaussian process regression could obtain the confidence interval of the fusion data, which is useful to the analysis of uncertainty.