Showing papers by "Don Harris published in 2021"
13 citations
TL;DR: A model of an air combat system is introduced, which explains the dynamically interacting elements relevant to the measurement of team performance in air combat and the associated measuring techniques can be applied to the analysis of any military system where the objective is to achieve a holistic measure ofteam performance.
Abstract: In air combat, a traditional way of evaluating team’s taskwork performance is to measure its performance output. However, it provides a narrow view about the team’s performance and potentially miss...
12 citations
TL;DR: A new LVC simulation framework for the development of air combat tactics, techniques, and procedures (TTP) is introduced, developed iteratively in separate C-, V-, and L-simulation stages to allow the utilization of the strengths of each simulation class while avoiding the challenges of pure LVC simulations.
Abstract: This paper advances live (L), virtual (V), and constructive (C) simulation methodologies by introducing a new LVC simulation framework for the development of air combat tactics, techniques, and pro...
10 citations
TL;DR: A new technique for the assessment of Team Situation Awareness (TSA) accuracy based upon post task Critical Decision Method structured interviews was developed and tested using 39 combat-ready F/A-18 pilots as mentioned in this paper.
8 citations
TL;DR: The use of the live (L), virtual (V), and constructive (C) simulation framework introduced in Part 1 of this two-part study is demonstrated in the testing and evaluation of air combat tactics, techniques, and procedures.
Abstract: In this paper, the use of the live (L), virtual (V), and constructive (C) simulation framework introduced in Part 1 of this two-part study is demonstrated in the testing and evaluation of air comba...
7 citations
TL;DR: In this article, a neural network was trained to categorize high and low-risk flight environments from factors such as the weather and pilot experience using data extracted from accident and incident reports, with negative outcomes used as markers of risk level.
Abstract: Flight risk assessment tools (FRATs) aid pilots in evaluating risk arising from the flight environment. Current FRATs are subjective, based on linear analyses and subject-matter expert interpretation of flight factor/risk relationships. However, a 'flight system' is complex with non-linear relationships between variables and emergent outcomes. A neural network was trained to categorize high and low-risk flight environments from factors such as the weather and pilot experience using data extracted from accident and incident reports. Negative outcomes were used as markers of risk level, with low severity outcomes representing low-risk environments and high severity outcomes representing high-risk environments. Eighteen models with varied architectures were created and evaluated for convergence, generalization and stability. Classification results of the highest performing model indicated that neural networks have the ability to learn and generalize to unseen accident and incident data, suggesting that they have the potential to offer an alternative to current risk analysis methods.
2 citations