Bayesian Search Study for USW
Abstract
Adversarial submarine activity in the Atlantic has steadily intensified over the last few years. Furthermore, strategic adversaries have developed sophisticated and stealthy submarines making them much more difficult to locate. The heightened activity coupled with advanced platforms have allowed the United States' adversaries to challenge its dominance in the underwater domain. Though extensive research has been performed on optimized search strategies using Bayesian search methods, most methodologies in the open literature focus on search for stationary objects rather than a search for a moving Red submarine conducted by a Blue submarine. Thusly motivated, we develop a model of an enemy submarine whose goal is to avoid detection. As the search effort is expended, a posterior probability distribution for the enemy submarines location is calculated based off negative search results. We present a methodology for finding a search pattern that attempts to maximize the probability of detection in a Bayesian framework utilizing Markovian properties. Specifically, we study three different running window methods: a simple network optimization model, a network optimization model that performs updates after every time period planning the entire route, and a dynamic program that only looks two time periods ahead.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2022
- Accession Number
- AD1184543
Entities
People
- Moshe Kress
- Roberto Szechtman
Organizations
- Naval Postgraduate School