Session 2A: A preview of functional data analysis for modeling and simulation validation
Dr. Curtis Miller is a research staff member of the Operational Evaluation Division at the Institute for Defense Analyses. In that role, he advises analysts on effective use of statistical techniques, especially pertaining to modeling and simulation activities and U.S. Navy operational test and evaluation efforts, for the division's primary sponsor, the Director of Operational Test and Evaluation. He obtained a PhD in mathematics from the University of Utah and has several publications on statistical methods and computational data analysis, including an R package. In the past, he has done research on topics in economics including estimating difference in pay between male and female workers in the state of Utah on behalf of Voices for Utah Children, an advocacy group.
Modeling and simulation (M&S) validation for operational testing often involves comparing live data with simulation outputs. Statistical methods known as functional data analysis (FDA) provides techniques for analyzing large data sets ("large" meaning that a single trial has a lot of information associated with it), such as radar tracks. We preview how FDA methods could assist M&S validation by providing statistical tools handling these large data sets. This may facilitate analyses that make use of more of the data available and thus allows for better detection of differences between M&S predictions and live test results. We demonstrate some fundamental FDA approaches with a notional example of live and simulated radar tracks of a bomber’s flight.