Award Data

SI2 proposes to leverage prior Government funded efforts to develop a transmit (TX) only, wideband (6:1 bandwidth), low-cost, scalable active electronically scanned array (AESA) that will utilize phase-only nulling to enable radar, Electronic Warfare / Electronic Attack (EW/EA), Information Operations (IO) and other capabilities on multiple platforms across DOD agencies. The array will employ digi ...

Few-shot Object detection via Reinforcement Control of Image Simulation (FORCIS) will combine deep reinforcement learning with additional training data augmentations and strategies to develop robust few shot detectors leveraging available simulations.

Airborne radars operating over open water must classify maritime vessels by measuring and exploiting highly-variable radar signatures. Sources of signature variability include within-class ship construction and equipment differences, complex in-situ 6-DoF ship motion caused by ocean waves across a range of sea state conditions, acquisition geometry including grazing angle and maritime-specific RF ...

Deep Neural Networks (DNNs) are becoming widely used in the DoD for image classification, but recent research has shown DNNs are vulnerable to adversary attacks. Adversaries can monitor the DNN training and classification processes to learn attributes of the training data and the DNN. With this information, an adversary can gain valuable insight into the potentially sensitive data used to train th ...

Program protection and security personnel in the DoD are faced with the complex task of identifying and protecting sensitive information about systems, including the nature of mission-critical functions and components, and Critical Program Information (CPI). Successful and efficient identification and protection of CPI is essential to maintaining US Warfighters’ technological advantage. Cur ...

Ground vehicles with navigation capability (e.g., GPS) can index into foundation data (e.g., Google Maps) to gain situational awareness abouttheir surroundings. When GPS and RF navigation sources are degraded, maintaining situational awareness requires an alternative navigationsource. One alternative source is the foundation data itself. The data contain objects at known 3D locations, which projec ...

Generalized change detection is a critical capability to mitigate the need for massive human inspection of the rapidly expanding volume ofglobal overhead satellite imagery. Current optical change detection approaches focus on fully specified systems to detect a predefined set ofchanges, and effective approaches for generalized change detection have not yet been demonstrated. We propose to build a ...

This SBIR Phase I project proposes a probabilistic approach to determine a vehicles location using onboard video sensors and foundationalmap data. The system does not rely on only one type of information source, instead it combines proposals from a variety of locationestimators to find a vehicles location in GPS-denied environments.The system takes advantage of recent advancements in computer visi ...

Deep Learning Analytics (DLA) will develop the Deep-False Alarm Suppression Technique (D-FAST) algorithm that uses state of the art and

This SBIR Phase II project will develop biologically inspired computational models and algorithms to enable low-shot and one-shot detectionof objects-of-interest in remote sensing imagery. The Phase II effort will build upon our Phase I work including multi-scale representationlearning framework and deep-learning based feature extraction and matching techniques for low-shot target detection. The P ...