Title: Adaptive sequential estimation with unknown noise statistics
Abstract: 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.
Publication Year: 1976
Publication Date: 1976-08-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 534
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot