Title :
Reduced order models for smoothing errors
Author_Institution :
The Analytic Sciences Corporation, Reading, Massachusetts
Abstract :
This paper describes a technique for modeling the time correlations of the smoothing error process, i.e., the stochastic process consisting of a linear system driven by gaussian noise. The reduced order model is developed via shaping filter design. This technique can be used to separate a large-scale linear smoothing problem into stages, each of which consists of a subsystem smoothing problem.
Keywords :
Algorithm design and analysis; Autocorrelation; Covariance matrix; Extraterrestrial measurements; Filters; Reduced order systems; Satellites; Smoothing methods; Stochastic processes; Vectors;
Conference_Titel :
Decision and Control including the 15th Symposium on Adaptive Processes, 1976 IEEE Conference on
Conference_Location :
Clearwater, FL, USA
DOI :
10.1109/CDC.1976.267688
