Mr. Thomas M. Booth Profile

Thomas M. Booth

at US Air Force

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Proceedings Article | 14 June 2023 Presentation + Paper

Machine learning model cards toward model-based system engineering analysis of resource-limited systems

Thomas Booth, Sudipto Ghosh

Proceedings Volume 12547, 125470J (2023) https://doi.org/10.1117/12.2678355

KEYWORDS: Data modeling, Systems modeling, Performance modeling, Sensor fusion, Sensors, Education and training, Monte Carlo methods, Model-based design, Machine learning

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Sensor fusion combines data from a suite of sensors into an integrated solution that represents the target environment more accurately than that produced by individual sensors. New developments in Machine Learning (ML) algorithms are leading to increased accuracy, precision, and reliability in sensor fusion performance. However, these increases are accompanied by increases in system costs. Aircraft sensor systems have limited computing, storage, and bandwidth resources, which must balance monetary, computational, and throughput costs, sensor fusion performance, aircraft safety, data security, robustness, and modularity system objectives while meeting strict timing requirements. Performing trade studies of these system objectives should come before incorporating new ML models into the sensor fusion software. A scalable and automated solution is needed to quickly analyze the effects on the system’s objectives of providing additional resources to the new inference models. Given that model-based systems engineering (MBSE) is a focus of the majority of the aerospace industry for designing aircraft mission systems, it follows that leveraging these system models can provide scalability to the system analyses needed. This paper proposes adding empirically derived sensor fusion RNN performance and cost measurement data to machine-readable Model Cards. Furthermore, this paper proposes a scalable and automated sensor fusion system analysis process for ingesting SysML system model information and RNN Model Cards for system analyses. The value of this process is the integration of data analysis and system design that enables rapid enhancements of sensor system development.

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