A Bayesian Approach to Estimating Detection Performance in a Multi-Sensor Environment

Abstract

This report presents the results of Operational Research support to maritime surveillance operations in the Canadian maritime approaches. The principal development is a Bayesian method to estimate the performance of sensors in a way that can be applied during ongoing operations. This novel method does not require knowledge of the sea-truth to evaluate sensors. A method to estimate sea-truth is also presented, which provides a new and unique analysis capability for the estimation of surveillance gaps. The series of new metrics and methods are implemented in a variety of operational tools for analysis of the recognized maritime picture (RMP), which support new operational procedures and processes. The desired result is a more robust and optimized maritime surveillance capability.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
AD1018042

Entities

People

  • Antony Zegers
  • D. W. Mason
  • Edward J. Edmond
  • Steven A. Horn

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Bayesian Networks
  • Classification
  • Computational Science
  • Detection
  • Detectors
  • Identification Systems
  • Information Science
  • Maritime Domain Awareness
  • Mathematics
  • Monte Carlo Method
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Surveillance
  • Surveys
  • Target Recognition

Readers

  • Maritime and Naval Warfare Studies
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy