Adaptive Particle Filter Algorism for Flutter Test with Variable Progression Speed

Abstract

Signal collected in flutter test with variable progression speed (FTVPS) is usually non-stationary, both its frequency and amplitude changed dramatically with time, especially in the sub-critical state. The common non-stationary signal processing method, such as time-varying parameter modeling can hardly analyze and track the mode of signal precisely under high non-stationary degree. Therefore, an adaptive particle filter method based on non-stationary degree is proposed. The tracking performance under high non-stationary degree of this method is verified by simulation experiment data. The results indicate that the method proposed in this thesis has better precision under high non-stationary degree when compared with usual particle filter.

Keywords

• ftpvs
• non-stationary signal processing
• time-varying parameter modeling method
• non-stationary degree
• adaptive particle filter algorism