The Application of Kalman Filter on the Initial Alignment Algorithm of Strapdown Navigation System on Stationary Base

Initial alignment is the process whereby the orientation of the axes of an inertial navigation system is determined with respect to the reference system. In this paper, the initial alignment error equations of the strapdown inertial navigation system (SINS) have been presented and discussed. The observability of SINS error models are discussed, and then a reduced order Kalman filter with five states and a full order Kalman filter with eight states are designed respectively to estimate the states of error models. It is shown that not all of these states are observable, and those states which are observable are different in rate of convergence. Results of the simulation show that the reduced order Kalman filter can guarantee higher accuracy and has less calculating burden compared with the full order Kalman filter.