【作者】 钟雄虎；

【导师】 裴承鸣；

【作者基本信息】 西北工业大学 ， 信号与信息处理， 2006， 硕士

【摘要】 非平稳信号分析是现代数字信号处理的一个重要分支,而基于时变参数建模的自适应时频分析是处理非平稳信号的一个有效工具。目前,如何在非平稳性很高甚至是模型出现非线性的情况下对时变信号进行建模是一个难点问题,也是一项具有积极学术意义和明确应用价值的课题。 本文在系统学习现有时变参数建模相关理论的基础上,系统研究了基于粒子滤波的建模思想和基本方法。通过引入遗忘因子概念,提出了基于遗忘因子的卡尔曼粒子滤波方法,在一定程度上缓解了传统方法在参数估计过程中的粒子衰竭问题。通过扩展状态变量,改进了基于辅助变量的无轨迹卡尔曼粒子滤波器的性能,有效地解决了模型非线性带来的问题。论文还通过数字仿真对所研究方法的数值特性进行了分析研究。 根据颤振试验的原理和特点,本文将基于粒子滤波的时变参数建模方法引入到飞机结构亚临界响应分析的颤振边界预测(FBP)研究当中,提出了适用于连续变速颤振试验的FBP方法。经数字仿真及在某型飞机气弹模型低速风洞颤振试验数据处理,验证了本文方法的可行性、可靠性和工程实用性。 本文数值仿真与分析软件基于MATLAB软件和LABVIEW虚拟仪器设计开发。更多还原

【Abstract】 Nonstationary signal processing is an important branch in modern digital signal analysis. As an effective tool for dealing with nonstationary signal, adaptive time frequency analysis based on time-varying parameter modeling is rapidly developed and widely used. When the stationarity of the signal is pool or even model is nonlinearity, how-to complete the signal modeling is especially focused at present.In this thesis, the modeling methods applying particle filter is investigated based on the existed theory of time-varying parameter estimation. Firstly, to limit the particle degeneracy, a new method of Kalman particle filter is proposed by introducing forgetting factor. Second, to improve the performance of the parameter estimation, another improved particle filter is developed using the state vector extension in auxiliary unscented. The analysis of the numerical characteristics of the methods is completed by computer simulation. The results show that there are many advantages for the methods presented here comparing to the traditional approach.Focused on the features and requirements of flutter test data processing, the paper’s method is introduced for flutter boundary prediction under turbulence excitation. Simulation and flutter test of low-speed wind-tunnel of one aeroelastical mode are used to examine the specifics of the scheme. Especially, the technique can be expanded to the flutter test with variable speed.The related software is developed on Matlab6.5 and Labview7.1.更多还原