Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Timothy W. Holzmann, PhD

Abstract

Despite decades of research, maritime traffic models’ limited predictive power continues to constrain their operational utility. We build on previous pattern of life modeling algorithms and contribute a Bayesian inferencing model for anomaly detection. Our probabilistic approach provides decision makers the capability to tailor the belief threshold for identifying anomalies and to enact a measured response based on the degree of abnormality. We perform a case study to evaluate the results, verifying that our Bayesian inferencing method accurately refines its probability when given the location and time of a ship of interest, and serving as a proof of concept for providing actionable information to respond to abnormal behavior.

AFIT Designator

AFIT-ENS-MS-23-M-130

Comments

A 12-month embargo was observed.

Approved for public release. Case number on file.

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