Publication Date

2017

Document Type

Dissertation

Committee Members

Michael Bryant (Committee Member), Fred Garber (Committee Member), Randolph Moses (Committee Member), Brian Rigling (Advisor), Kefu Xue (Committee Member)

Degree Name

Doctor of Philosophy (PhD)

Abstract

In this dissertation, a novel algorithm, SV-Means, is developed motivated by the many functions needed to perform radar waveform classification in an evolving, contested environment. Important functions include the ability to: reject classes not in the library, provide confidence in the classification decision, adapt the decision boundary on-the-fly, discover new classes, and quickly add new classes to the library. The SV-Means approach addresses these functions by providing a fast algorithm that can be used for anomaly detection, density estimation, open set classification, and clustering, within a Bayesian generative framework. The SV-Means algorithm extends the quantile one-class support vector machine (q-OCSVM) density estimation algorithm into a classification formulation with inspiration from k-means and stochastic gradient descent principles. In addition, the algorithm can be trained at least an order of magnitude faster than the q-OCSVM and other OCSVM algorithms. SV-Means has been thoroughly tested with a phase-modulated radar waveform data set, and several data sets from the University of California Irvine (UCI) machine learning repository, in each application area except clustering. In clustering, a novel algorithm, SV-Means Level Set Clustering, was formulated using the SV-Means algorithm as a first step to determine the number of clusters per level set and distinguish overlapping clusters. Finally, an end-to-end demonstration from training, to testing, to clustering, to adding a new class to the library, was demonstrated using the SV-Means algorithm.

Page Count

103

Department or Program

Department of Electrical Engineering

Year Degree Awarded

2017

ORCID ID

0000-0002-1925-7231

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