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Stochastic process approximation for recursive estimation with guaranteed bound on the error covarianceAn approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.
Document ID
19760009797
Acquisition Source
Legacy CDMS
Document Type
Technical Memorandum (TM)
Authors
Menga, G. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
September 3, 2013
Publication Date
September 1, 1975
Subject Category
Statistics And Probability
Report/Patent Number
NASA-TM-X-73078
A-6295
Accession Number
76N16885
Funding Number(s)
PROJECT: RTOP 505-07-11
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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