Assessing Resource Requirements for Maritime Domain Awareness and Protection (Security)

Abstract

A maritime domain or region contains a number w of nonhostile W (White) vessels of interest. Hostile R (Red) vessels enter the domain. The Rs are traveling through the domain toward targets. Overhead, friendly (Blue) sensors ( S) patrol the domain and classify (perhaps incorrectly) detected vessels of interest as R or W. The misclassification of a W as an R is a false positive. An overhead sensor follows (or tracks) any vessel it classifies as R until it is relieved by another platform, perhaps a destroyer pair (DD). The overhead sensor is here assumed unable to detect and classify additional vessels while it is following a suspicious vessel; this may well be a somewhat pessimistic assumption, very possibly richer possibilities based on additional assets (such as unmanned aerial vehicles (UAVs)) are available, but loss of track may occur as well as misclassification. Deterministic and stochastic models are formulated and studied to evaluate the probability that Rs are successfully neutralized before reaching their destination. The model results quantify the effect of the resources and time needed to prosecute misclassified Ws (false positives) on the probability of successfully neutralizing R. The results indicate that the probability of neutralizing an R vessel is very sensitive to the false positive rate. Technologies, processes, and procedures that can decrease the false positive rate will increase the effectiveness of the Maritime Intercept Operation (MIO).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA456280

Entities

People

  • Donald P. Gaver Jr.
  • Hiroyuki Sato
  • Patricia A. Jacobs

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Automatic Identification Systems
  • Communication Systems
  • Detection
  • Differential Equations
  • Equations
  • Identification
  • Identification Systems
  • Maritime Domain Awareness
  • Markov Models
  • Operations Research
  • Platforms
  • Probability
  • Random Variables
  • Security
  • Systems Engineering
  • Unmanned Aerial Vehicles

Readers

  • Maritime Security/Maritime Homeland Security
  • Regression Analysis.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • Autonomy
  • Autonomy - UAVs