Award Data

ABSTRACT: It is well known that wind turbine clutter (WTC) presents a significant challenge to detecting targets in civilian and military applications. The large radar cross section (RCS) of wind turbines, combined with their significant range of Doppler spread, make traditional clutter mitigation techniques effectively useless. The objective of this Phase I effort is to develop a physically con ...

ABSTRACT: Space defense analysis and mission trade studies are vital for the success of space-borne military operation. In this proposal, Intelligent Fusion Technology, Inc (IFT) and its partners propose a decision support tool called stochastic adaptive nonlinear differential game tool (SANDGT) for persistent and risk-advert space situation awareness. The main idea of our approach is to design a ...

ABSTRACT: Intelligent Automation, Inc. (IAI) proposes to apply a game theoretic approach to design a robust decision making tool for self defense space systems that integrates realistic system models, distributed sensor management, advanced estimation, and tracking techniques. Our approach uses innovative game models to track space objects, analyze their orbits, and provide decision tools for the ...

ABSTRACT: The overall program objective is to utilize the best available technologies to successfully develop the concept and design of a state of the art C-Band telemetry transmitter that meets or exceeds the performance qualification parameters and size constraints detailed in the AF12-BT07 solicitation. Specifically, the objective of the entire two-phase program is to develop, test, and deliv ...

ABSTRACT: Barron Associates Inc. proposes development of a Game-based AutonoMy-Enabled Response (GAMER) system for spacecraft protection. The effort extends leading-edge research in dynamic games to enable autonomous defensive response to space threats. GAMER introduces a hierarchical approach that enables multiple satellites to work together towards a common protection strategy in an evolving en ...

ABSTRACT: The objective of this effort is to develop innovative methods for deriving a sparse set of physical target features that can be used for exploitation of air to ground signature data collected from sensor systems including electro-optical, infrared, and laser radar. Current classification methods require near exact replication of the original imaging parameters, or extensive modeling in ...

ABSTRACT: Existing machine learning algorithms have difficulty using all available data about a problem. This STTR will develop a new algorithm that can make full use of all available data, whether that data is labeled or not, and even when some data types or data resolutions are not available during operation. BENEFIT: This STTR will develop a novel machine learning algorithm for reasoning abo ...

ABSTRACT: Detection of clandestine tunnels and underground facilities is a continuing interest of the US DoD and Customs and Border Protection. Research continues in an attempt to find functional and reliable sensing methods, including seismic methods. Dynamic activity in tunnels emits mechanical energy that propagates away in seismic waves. Resulting ground vibrations can be measured at offset d ...

ABSTRACT: Adversarial installations, such as those housing the means for nuclear weapons production, are increasingly being constructed in heavily fortified locations and often using ultra high performance concrete (UHPC) as the construction material. As such, the U.S. Air Force has considerable interest in further developments of ultra high performance concrete (UHPC) to maintain an advantage o ...

ABSTRACT: Feature extraction and target recognition suffer from a lack of a reliable model for both exploitable target features and the electromagnetic signature they possess. Signature data are often hard to interpret and invert to recover the target robustly. Bayesian learning approaches to statistical pattern recognition are based on the use of training sets of inputs and outputs, a data mode ...