连续变速颤振试验的自适应粒子滤波算法

连续变速颤振试验的采集信号通常为非平稳信号,其频率和幅值随时间变化,尤其在亚临界状态下, 变化程度十分剧烈。常用的非平稳信号时变参数建模分析方法,在信号非平稳程度较高的情况下难以对信号 的模态进行准确地分析和跟踪。为了解决这一问题,结合信号非平稳度量计算方法,提出一种改进的自适应粒 子滤波算法,并通过仿真实验数据对所提算法在高非平稳度情况下的跟踪性能进行验证。结果表明:与一般粒 子滤波算法相比,本文方法在高非平稳度情况下具有更高的跟踪精度。

Adaptive Particle Filter in Flutter Test with Variable Progression Speed

Signal collected in flutter test with variable progression speed is usually non-stationary, both its frequency and amplitude changed over time, especially in the sub-critical state. The common non-stationary signal processing method, such as time-varying parameter modeling can hardly analyze nor 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 suggest that method proposed in this thesis has better precision under high non-stationary degree when compared with original particle filter.