Research On Sins Initial Alignment With Randomly Delayed Observations

Initial alignment is one of the key technologies of the inertial navigation system, it's alignment accuracy directly affect the navigation system working performance. Filtering technique plays an extremely crucial role in the initial alignment and has an important effect to the system accuracy.Actual inertial system always works in rugged environment and often suffers interferences from various kinds of fators. Especially to the strap-down inertial navigation system (SINS), the intial heading error angle is relatively large and the error model is generally nonlinear. The nonlinear error model needs nonlinear filtering methods. In SINS initial alignment, there exist situation in which the arrival of the measured data is delayed and due to the system of uncertainty, the possibility of delay in each measurement is random. Therefore, it is very significant to research the nonlinear filtering methods using randomly delayed observations and it's application in SINS initial alignment in roder to improve the system performance.In this paper, SINS initial alignment with randomly delayed observations is researched. On the base of establishment of the model with one-step randomly delayed observations, Extended Filter and Unscented filter for nonlinear discrete-time systems using one-step randomly delayed observations contaminated by additive white noise are developed and are applied to initial alignment of SINS.The main research work in this thesis can be summarized as follows:Firstly, the basic theory of SINS initial alignment and the causes of SINS error are expatiated. The inertial sensor error models, velocity error equations, attitude error equations and position error equations of SINS are given respectively.Secondly, the nonlinear model of nonlinear discrere-time stochasic system using observations with stochastic delays contaminated by additive white noise is established and it's filtering promble is discussed. The Extended Filter (EF) based on linearizing system equations is derived and it's filtering equations are given.Thirdly, on the base of Unscented Transform (UT) theory and UT sampling strategy, Unscented Filter (UF) using one-step randomly delayed observations is derived and improved Unscented Filter based on SSUT is investigated.Fourthly, the nonlinear alignment model of SINS on moving bases is established, including system state equation and system observation equation using one-step randomly delayed observations. The EF and UF are applied and simulated to initial alignment of SINS. The simulation results show that the UF has faster convergent rate and better aligment precision than EF. Improved filtering has the same aligment precision with UF and it's computions are greatly reduced.