Award Data

As the orbital space around the earth gets more and more crowded with civilian and military reconnaissance, weather, tracking, communication, and navigation satellites - civilian and military users become increasingly dependent on the services provided by these spacecraft. This situation increases the possibility of offense actions against some or all of these spacecraft. The possibility calls for ...

The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...

Recent advances in the hypersonic system capabilities of adversary nations threaten the battlespace supremacy of US forces. Missile defense systems capable of extended high-g maneuvering at stratospheric altitudes and hypersonic Mach numbers will provide the MDA with additional flexibility in defending the US against emerging hypersonic threats. A key enabling technology for the deployment of such ...

The objective of this project is to develop a capability to model fracture of materials used in hypersonic vehicles that results from hypervelocity impact while exposed to extreme temperatures. Symplectic Engineering’s approach addresses this challenge at two levels. At the computational level, a Relaxed Extended Finite Element approach is pursued to represent (possibly intersecting) fractures l ...

This STTR research study aims to enhance and apply thermo-mechanically coupled computational models for high-temperature fracture. This topic is particularly challenging in that hypersonic flight in the atmosphere generates extreme conditions over a vehicle that can affect the strength and performance the vehicle materials, both in-flight conditions as well as for cases where the vehicle encounter ...

We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...

Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

Overhead Persistent Infrared (OPIR) platforms observe challenging threat and scene phenomenology. Toyon Research Corporation developed an image processing framework for clutter estimation/suppression and track-before-detect to jointly detect and track targets. The Target Extraction and Conjoint Tracking application, developed under initial Phase II funding and demonstrated using real-world data so ...

Current methods of validating the cyber resiliency of missile defense systems require testing of actual systems, which removes them from operation and subjects them to potentially damaging effects. Cyber ranges can be used as an alternative, but they are limited in scale, costly, time-consuming to configure and have limited capability to model wireless tactical networks and their inherent vulnerab ...

The proposed work will develop a massively parallelized architecture for Monte Carlo simulation of real-time fire control schedulers. The parallelized architecture will utilize the recent developments in multiprocessing/multithreading technology based on graphical processing units. The system will be designed to perform in real-time assuming non-collocated sensors observing the threats and then co ...