Automated Factor-Based Sensitivity Analysis

There is a large space of threat data, scenarios, and input variables required for understanding the behavior of Aegis Weapon System (AWS) designs like the Figure of Merit (FOM) algorithm used in the Weapon Control System (WCS) functions for Ballistic Missile Defense (BMD) intercept prediction function and pre-launch coordination. Developing an understanding of performance roll-offs and discontinuities can be exhaustive and time consuming. Machine learning, and the development of deep neural networks, has emerged as a revolutionary tool for the detection, classification, and generation of complex patterns in high-dimensional space. Due to a corresponding increase in both processing power and neural architecture efficiency, the application of machine learning to more restrictive domains have become possible. We propose an automated sensitivity analysis tool, leveraging GFI threat scenario data accessible through an integrated software environment (PULSE) to feed Machine Learning (ML) deep neural networks which generate data allowing FOM performance to be evaluated and categorized. One technique we propose is a Generative Adversarial Neural Network (GAN) that has been used to enhance simulations for subsequent training of anomaly-detection in highly sophisticated simulators for the NOAA GOES satellites. A GAN is a pair of competing neural networks that will find the slow degradation and sudden “anomalies” in performance in an intelligent manner through reinforcement learning. The integration of the ADS developed GAN framework within the SEG PULSE environment to evaluate FOM provides the opportunity to train the neural network in a robust fashion. Approved for Public Release | 20-MDA-10643 (3 Dec 20)