A robust empirical bayes approach to the adaptive Kalman filter.

Bibliographic Details
Main Author:	Eggers, Mitchell Donn
Other Authors:	Fischer, Thomas R. (degree committee member.), Lacey, H. Elton (degree committee member.), Longnecker, Michael T. (degree committee member.)
Format:	Thesis Book
Language:	English
Published:	1984.
Subjects:	Bayesian statistical decision theory. Kalman filtering. Electrical Engineering Academic theses
Online Access:	Link to ProQuest Copy Link to OAKTrust copy

Description
Abstract:	This work addresses a long-standing need to create a mathematically credible and consistent foundation for adaptive Kalman filtering. Since the original breakthrough by Kalman and Bucy in 1960, it has been recognized that some practical applications of their work require supplementary algorithms, not provided by the original derivation. In particular, algorithms are required to "adapt" the filter's gain element when encountering insufficient or incorrect knowledge of the required statistical parameters. The open literature of Kalman filtering contains many presentations of "Adaptive Kalman" algorithms. However, satisfying first-principles approaches for justifying such algorithms appear lacking. Without a consistent, rational foundation, the adaptive Kalman filter has remained an ad hoc development.
Item Description:	"Major subject: Electrical Engineering." Typescript (photocopy). Vita.
Physical Description:	x, 125 leaves : illustrations ; 29 cm
Bibliography:	Includes bibliographical references (leaves 115-118).