Date of Award

6-1991

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Mark Gallagher, Captain, USAF

Abstract

The discrete-time Kalman filter is a conditional mean estimator of the states of a linear stochastic process, conditioned on the previous state and current measurements. It assumes that the states and noise inputs can be represented as jointly Gaussian random variables. The influence diagram is a decision analysis tool. Under certain conditions, it can represent continuous, jointly Gaussian random variables. The conditioning order of the random variables may be changed (using Bayes' rule) so that any random variable can be conditioned on any other subset of random variables in the diagram. Under these conditions, an influence diagram can represent the states, measurements, and initial conditions of a linear stochastic process. It too can be a conditional mean estimator of the states of a linear stochastic process, and is an alternative algorithm for the Kalman filter. The influence diagram algorithm for the Kalman filter uses a factored covariance matrix. It is similar to other factored forms of the Kalman filter such as the U-D filter. It can be faster than other factored forms of the Kalman filter, but retain their improved numerical properties.

AFIT Designator

AFIT-GE-ENG-91J-06

DTIC Accession Number

ADA238443

Comments

The author's Vita page is omitted

Download

Share

COinS