Scenario analysis using machine learning to inform sensor design

Abstract

Target detection is a key component of any constructive simulation. Detecting the target is the first step in the beginning of the constructive simulation. Detection predictions can have significant effect on the results in any constructive simulation. This paper explores the utility of advances in machine learning to inform sensor design by analyzing the outcomes of the time and detection models in constructive simulations. A simple scenario will be designed, and execution of the scenario will be performed to create datasets for analysis. Traditional metrics such as probability of detection and time to detect will be evaluated by the algorithms to determine the optimal sensor(s) designs to achieve the best possible performance across the scenario. A summary of the results and recommendations for machine learning algorithm design for this type of data analysis will then be presented.