PROJECT TITLE :

Stochastic observability-based analytic optimization of SINS multiposition alignment

ABSTRACT:

The Kalman filter has always been applied to enhance the estimation of inertial measurement unit errors and to boost estimation accuracy of navigation states under practical conditions. Therefore, understanding the behaviors and limitations of optimal estimation of the navigation states is instructive and of nice importance. So as to supply comprehensive information concerning the observability and convergence rapidity of the navigation states when implementing a Kalman filter, the basic properties of intuitive linear-algebraic characterizations of stochastic observability can be intensively investigated in this study. We tend to have extended the utilization of the analytic stochastic observability approach for analytic optimization of strapdown inertial navigation systems multiposition stationary alignment. The advantage of analytic express formulation of convergence rapidity of the implemented Kalman filter by stochastic observability approach is demonstrated. Compared to numerical simulation ways, the proposed stochastic observability approach will provide analysts with abundant more analytic info.