Optimal State Estimation For Stochastic Systems With Measurement Delays And Auto-Correlated Noises

The problem of system observation and filter is an important research topic of modern control theory,which has been studied by many scholars at home and abroad.Among them,the estimation problem based on linear quadratic condition has a lot of research and related applications,The least mean square estimation and Kalman filter in linear mean square sense have important applications in aerospace,navigation and other control fields.Under the linear mean square framework,the uncertainty of the model of system,multichannel observation with time-delay,time-dependent noise and channel fading in transmission are all problems worthy of study,which are common in practical application and production life.In this thesis,the linear mean square optimal estimation of additive noise with multi-channel delay measurement and time correlation is studied,and the corresponding state estimator is designed.The organizational structure of the thesis is as follows:the first two chapters introduce the research background and some basic methods and theorems related to the least mean square estimation,Kalman filter and time-delay filter,The third chapter describes the main problems of the research and deduces the relevant conclusions,verifies the validity of the results through numerical simulation,the last chapter summarizes the whole thesis and looks forward to the future work.The main work and contributions of this thesis are as follows:firstly,the linear optimal estimation of multi-channel time-delay observation system with model uncertainty is considered,the corresponding state estimator is designed by means of measurement difference and innovation recombination;secondly,the optimal estimation of time-delay observation system with transmission channel fading is considered,similar methods and calculations are used,Then the corresponding state estimator is designed.