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Linear Covariance Analysis and Epoch State EstimatorsThis paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Document ID
20160009227
Document Type
Reprint (Version printed in journal)
Authors
Markley, F. Landis (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Carpenter, J. Russell (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
July 20, 2016
Publication Date
July 23, 2014
Publication Information
Publication: The Journal of the Astronautical Sciences
Volume: 59
Issue: 3
Subject Category
Statistics and Probability
Report/Patent Number
GSFC-E-DAA-TN33019
Distribution Limits
Public
Copyright
Other
Keywords
Linear covariance analysis
Consider parameters
Epoch state estimators