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Optimal gains for a single polar orbiting satelliteGains are the spatial weighting of an observation in its neighborhood versus the local values of a model prediction. They are the key to data assimilation, as they are the direct measure of how the data are used to guide the model. As derived in the broad context of data assimilation by Kalman and in the context of meteorology, for example, by Rutherford, the optimal gains are functions of the prediction error covariances between the observation and analysis points. Kalman introduced a very powerful technique that allows one to calculate these optimal gains at the time of each observation. Unfortunately, this technique is both computationally expensive and often numerically unstable for dynamical systems of the magnitude of meteorological models, and thus is unsuited for use in PMIRR data assimilation. However, the optimal gains as calculated by a Kalman filter do reach a steady state for regular observing patterns like that of a satellite. In this steady state, the gains are constants in time, and thus could conceivably be computed off-line. These steady-state Kalman gains (i.e., Wiener gains) would yield optimal performance without the computational burden of true Kalman filtering. We proposed to use this type of constant-in-time Wiener gain for the assimilation of data from PMIRR and Mars Observer.
Document ID
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Banfield, Don
(California Inst. of Tech. Pasadena, CA, United States)
Ingersoll, A. P.
(California Inst. of Tech. Pasadena, CA, United States)
Keppenne, C. L.
(Jet Propulsion Lab. California Inst. of Tech., Pasadena., United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1993
Publication Information
Publication: Lunar and Planetary Inst., Workshop on Atmospheric Transport on Mars
Subject Category
Spacecraft Design, Testing And Performance
Accession Number
Distribution Limits
Work of the US Gov. Public Use Permitted.

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