KEYWORDS: Monte Carlo methods, Systems modeling, Data modeling, Situational awareness sensors, Process modeling, Computer simulations, Modeling and simulation, Performance modeling, Lead, Analytical research
Modeling and simulation has been established as a cost-effective means of supporting the development of requirements,
exploring doctrinal alternatives, assessing system performance, and performing design trade-off analysis. The Army's
constructive simulation for the evaluation of equipment effectiveness in small combat unit operations is currently limited
to representation of situation awareness without inclusion of the many uncertainties associated with real world combat
environments. The goal of this research is to provide an ability to model situation awareness and decision process
uncertainties in order to improve evaluation of the impact of battlefield equipment on ground soldier and small combat
unit decision processes. Our Army Probabilistic Inference and Decision Engine (Army-PRIDE) system provides this
required uncertainty modeling through the application of two critical techniques that allow Bayesian network technology
to be applied to real-time applications. (Object-Oriented Bayesian Network methodology and Object-Oriented Inference
technique). In this research, we implement decision process and situation awareness models for a reference scenario
using Army-PRIDE and demonstrate its ability to model a variety of uncertainty elements, including: confidence of
source, information completeness, and information loss. We also demonstrate that Army-PRIDE improves the realism of
the current constructive simulation's decision processes through Monte Carlo simulation.
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.
Modern combat aircraft pilots increasingly rely on high-level fusion models (JDL Levels 2/3) to provide real-time
engagement support in hostile situations. These models provide both Situational Awareness (SA) and Threat
Assessment (TA) based on data and the relationships between the data. This information represents two distinct classes
of uncertainty: vagueness and ambiguity. To address the needs associated with modeling both of these types of data
uncertainty, an innovative hybrid approach was recently introduced, combining probability theory and possibility theory
into a unified computational framework. The goal of this research is to qualitatively and quantitatively address the
advantages and disadvantages of adopting this hybrid framework as well as identifying instances in which the combined
model outperforms or is more appropriate than more classical inference approaches. To accomplish this task, domain
specific models will be developed using different theoretical approaches and conventions, and then evaluated in
comparison to situational ground truth to determine their accuracy and fidelity. Additionally, the performance tradeoff
between accuracy and complexity will be examined in terms of computational cost to determine both the advantages and
disadvantages of each approach.
KEYWORDS: Data modeling, Probability theory, Fuzzy logic, Data fusion, Mathematical modeling, Weapons, MATLAB, Data acquisition, Integration, Process modeling
Fusion 2+ of air-to-air engagement involves pressing, real-time constraints and very large amounts of imperfect data. Real-time data acquired during an air-to-air engagement will have different types of imperfection; two representative classes of imperfection are vagueness and ambiguity in the data. However, the current approaches of managing Fusion 2+ are limited to utilize either vague data or ambiguous data. The most popular fusion technique for vague data is Fuzzy Logic, and for ambiguous data, the Bayesian Network. The challenge addressed in this proposal is to explore the framework of a hybrid processing Fusion 2+ model that can formally process both vague (fuzzy) and ambiguous (probabilistic) data types. There are two major issues for building this Fusion 2+ model. The first issue is to mathematically integrate the heterogeneous models, which have different domains, probability and possibility. The second issue is to programmatically integrate two different S/Ws. For solving the first issue, this research explores and adopts two novel transformation methods between probability and possibility and compares the sensitivity between methods. Also this research provides an Object Oriented Tool for building a hybrid model by adopting an Application Programming Interface, so that we can model the complex (multi-to-multi) Fusion 2+ model of an air-to-air engagement.
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