Engineering Commons^™

Application Of Sequence Comparison Methods To Multisensor Data Fusion And Target Recognition, Edmund W. Libby

Theses and Dissertations

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 …