A Digital Engineering Approach to Testing Modern AI and Complex Systems

Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this paper we introduce a Digital Engineering (DE) approach to T&E (DE-T&E), combined with generative AI, that can achieve requisite mil spec statistical validation as well as uncover potential deleterious Black Swan events that might otherwise not be uncovered until it is too late. An illustration of these concepts is presented for an advanced modern radar example employing deep learning AI.

High Fidelity RF Clutter Modeling and Simulation

In this paper, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation (M&S) techniques. Traditional statistical approximation based methods will be reviewed followed by more accurate physics-based stochastic transfer function clutter models that facilitate site-specific simulations anywhere on earth. The various factors that go into the computation of these transfer functions will be presented, followed by several examples across multiple RF applications. Finally, we introduce a radar challenge dataset generated using these tools that can enable testing and benchmarking of all cognitive radar algorithms and techniques.