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Proceedings Paper

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+
Author(s): K. David Lee; Eric Wiesenfeld; Andrew Gelfand
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Paper Abstract

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Paper Details

Date Published: 15 May 2007
PDF: 10 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 656714 (15 May 2007); doi: 10.1117/12.717398
Show Author Affiliations
K. David Lee, Decisive Analytics Corp. (United States)
Eric Wiesenfeld, Decisive Analytics Corp. (United States)
Andrew Gelfand, Decisive Analytics Corp. (United States)

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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