Improved and More Robust Automatic Target Classifiers

R-DEX Systems (R-DEX) proposes to leverage its unique expertise in advanced sensor solutions, Artificial Intelligence (AI), and Machine Learning (ML) to develop a groundbreaking technology for improved and more robust automatic target classifiers. The proposed approach will leverage R-DEX’s proprietary neural networks and the latest published literature. Specifically, R-DEX will incorporate its proprietary networks in published approaches for translating noisy observations in space and time into non-linear partial differential equations. The resulting deep hidden physics layers will form the basis for new classes of algorithms with a priori built in knowledge of the underlying physical laws. This innovative approach will allow better and more robust extrapolation to other objects within the same spatiotemporal framework using limited observations and/or augmented with computer and scaled model data. In Phase I, R-DEX will demonstrate the scientific and technical feasibility of the proposed approach using real and simulated data and will generate a technical development plan to develop a Phase II prototype and conduct a series of Phase II verification and validation experiments.