Application of Kalman filtering to the calibration and alignment of inertial navigation systems

Abstract

Problem areas and practical solutions in the development of large-dimension Kalman filters for the calibration and alignment of complex inertial guidance systems are discussed. The basic vector attitude error equation is augmented by gyro and accelerometer unknown parameters. The parameter estimation problem is converted into a state estimation problem. A complete approach and description of the dual extended Kalman filter, one for accelerometers and one for gyros, is given. To reduce computational load, a technique of prefiltering (data compression or measurement averaging) has been implemented in the mechanization with very little degradation in the performance of the filter. The models of gyros and accelerometers used are described in detail. A technique for generating parameter excitation trajectories which provides observability of instrument parameters has been developed by maximizing the information matrix. A typical set of results for a simulator data set for parameter estimates and innovation sequences is given to show the performance, convergence, accuracy, and stability of the filter estimates.<>