Abstract

Traditional techniques used for verification, validation, and accreditation (VV&A) of Synthetic Natural Environments for military applications are time consuming, subjective, and often costly. Due to varying levels of common visual factors, Synthetic Natural Environments (SNE) vary widely in appearance and use case. Early identification of these factors in the SNE life cycle may improve its Visual Aesthetic Quality (VAQ) while reducing VV&A issues downstream and informing future development. This research explores supplementing existing VV&A techniques with the Delphi Method during the conceptualization phase of an interoperable SNE development in order to identify the level of importance of SNE VAQ factors for distributed, dissimilar simulations earlier in the life cycle. Delphi Method findings on VAQ factors drove the development of four different SNEs for a selected urban city center. The importance of VAQ factors within the SNEs were derived through Conjoint Analysis of data from a survey in which end user participants evaluated each SNE using a design that incorporated fractional factorial screening and Graeco-Latin Squares. Research findings suggest: (1) using an online Delphi Method enables early identification of a correlated set of expertly accepted primary VAQ factors that affect overall realism and training utility in the virtual domain; (2) Conjoint analysis improves the understanding of the significance and power of identified factors and preferences; (3) VAQ importance rankings differed across the Delphi Method and Conjoint Analysis, nor did the Delphi Method successfully predict the two-factor interactions discovered through Conjoint Analysis of the screening design; and (4) Data mining of historical SNE issue reports did not identify the same level of importance of VAQ factors as users reviewing SNE representations through a Conjoint Analysis and Delphi panel expert forecasts. Limitations with the proposed technique, as well as recommendations for additional research are provided to further refine the parameters associated with these subjective factors to increase the efficiency and application of the proposed approach.

Notes

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Graduation Date

2020

Semester

Fall

Advisor

Proctor, Michael

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Degree Program

Modeling and Simulation

Format

application/pdf

Identifier

CFE0008341; DP0023778

URL

https://purls.library.ucf.edu/go/DP0023778

Language

English

Release Date

December 2021

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

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