A growing memory discrete dynamic model for performing temporal extrapolations along a predetermined path in a random field is presented. This dynamic model is used to drive a linear system that is itself driven by discrete white noise. The coupled system is used to derive a state estimation scheme that recursively processes noisy measurements of the system. Using the dynamic model as a reference (truth) model, the authors develop a covariance analysis to measure the estimation errors that occur when the dynamics along the path through the field are modeled as a Markov linear model and state estimation is performed using discrete Kalman filtering. The performance evaluation of an inertial navigation system influenced by the Earth's gravity field aboard a maneuvering ship is provided as a specific illustrative example.
State estimation and divergence analysis
Zustandsabschaetzung und Divergenz-Analyse
IEEE Transactions on Aerospace and Electronic Systems ; AES-20 , 5 ; 594-602
1984
9 Seiten, 9 Quellen
Aufsatz (Zeitschrift)
Englisch
State Estimation and Divergence Analysis
IEEE | 1984
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