Hypersonic wind tunnel aerodynamic identification method considering noise suppression

There are still many problems in load identification of non-stationary signals of full-size model test in wind tunnel test. A full scale model test of non-stationary signal load identification was proposed based on a deep residual shrinkage network （DRSN） deep learning technology of intelligent load identification method. This method extracted load system output data of aerodynamic force and inertial force and noise characteristics by deep learning， through attention mechanism it obtained data access threshold for each group， then the soft threshold function was used to filter the characteristics and reduce the noise. The inertial force component in the response signal of the force measurement system was identified and eliminated effectively， so as to realize the identification of aerodynamic load. In the test and verification， the identification accuracy of the mean value method was above 85%， and that of the DRSN model was above 94%， proving that the DRSN model can effectively reduce the interference of noise and inertia force on the load identification. It presented the characteristics of high accuracy and good reliability for the load identification of non-stationary signals. © 2023 BUAA Press. All rights reserved.