Impact of correlation errors on optimum Kalman filter matrices gains identification in multicoordinate systems

This paper investigates the impact that errors in the innovation correlation calculations has upon the steady-state Kalman filter gain identification. This issue arises in all real time applications, where the correlations must he calculated from experimental data. The algorithm proposed by [L. Hong (1991)] is considered and equations describing the impact are established. Simulation results are presented and discussed. Finally, experimental results for the algorithm in [L. Hong (1991)], applied to estimate the states of a servo system, are presented.