The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of
performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently
meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing
data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an
environment, centralized fusion approaches will have limited application due to the constraints of real-time
communications networks and computational resources. To overcome these limitations, we are developing a formalized
architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created
and managed information network. This network will support the incorporation and utilization of low level tracking
information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The
framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of
interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.
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.