A Bayesian Method for Managing Uncertainties Relating to Distributed Multistatic Sensor Search

Abstract

Predicting the search effectiveness of a distributed multistatic sensor field is highly conditioned on information which is unknown and, for all practical intents, unknowable when engaged in a two-sided tactical situation. Yet, it is imperative to have a method for assessing the military value of such systems to inform decisions relating to procurement, optimal employment, and maximal military exploitation. The combination of Monte Carlo simulation methods and Bayesian fusion techniques allow for a robust approach for modeling the effects of uncertainty on the distribution of likely outcomes. Exemplar analysis for an Area Clearance and an Area Denial scenario demonstrate how a combined Monte Carlo simulation and Bayesian fusion system might be employed to account for uncertainty and the types of information products they can provide a decision-maker.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA523579

Entities

People

  • B. I. Incze
  • Steven B. Dasinger

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Area Denial
  • Bayes Theorem
  • Bayesian Networks
  • Data Fusion
  • Detection
  • Geometry
  • Markov Processes
  • Monte Carlo Method
  • Multistatic Detection
  • Probability
  • Reliability
  • Simulations
  • Sonar
  • Stochastic Processes
  • Target Strength
  • Uncertainty
  • Undersea Warfare

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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