Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Department of Electrical and Computer Engineering

First Advisor

Peter S. Maybeck, PhD


This research addresses methods for exploiting the joint likelihood of observed kinematic and nonkinematic (sensor signature) physical events to improve dynamic object and target recognition. A principal direction is the application of dynamic programming sequence comparison techniques to condition matching of object signatures to known models according to observed kinematics. A second direction is the application of kinematic/aspect-angle Kalman filter trackers to condition kinematic tracking according to observed signatures. These conditioning processes dramatically reduce ambiguity in object recognition, and can be used together or separately to allow computation of a posterior probabilities of object class membership using Bayesian methods. Proposals are supported by simulated target tracking and high range resolution radar target recognition. Original contributions of this effort include: (1) new approaches for and theoretical understanding of syntactic methods in multisensor fusion and dynamic object recognition; and (2) extension of estimation and tracking techniques to allow object recognition and establish performance bounds for recognition.

AFIT Designator


DTIC Accession Number



The author's Vita page is omitted.

Page 3-27 of this dissertation is the author-corrected version of that page, as sent to DTIC in October 1995.