Key points are not available for this paper at this time.
Sequential estimators are derived for suboptimal adaptive estimation of the unknown a priori state and observation noise statistics simultaneously with the system state. First- and second-order moments of the noise processes are estimated based on state and observation noise samples generated in the Kalman filter algorithm. A limited memory algorithm is developed for adaptive correction of the a priori statistics which are intended to compensate for time-varying model errors. The algorithm provides improved state estimates at little computational expense when applied to an orbit determination problem for a near-earth satellite with significant modeling errors.
Myers et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: