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Adaptive sequential estimation with unknown noise statistics

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2 Author(s)
K. Myers ; Air Force Avionics Laboratory, Wright Patterson Air Force Base, OH, USA ; B. Tapley

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.

Published in:

IEEE Transactions on Automatic Control  (Volume:21 ,  Issue: 4 )