Context Aided Tracking with Adaptive Hyperspectral Imagery

A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in 5% the time of the baseline {a 95% reduction in sensor acquisition time. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. The context-aided system is demonstrated in a high- fidelity tracking testbed, and reduces track identity error by 30%

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere

Vehicle Tracking and Classification via 3D Geometries for Intelligent Transportation Systems

In this dissertation, we present generalized techniques which allow for the tracking and classification of vehicles by tracking various Point(s) of Interest (PoI) on a vehicle. Tracking the various PoI allows for the composition of those points into 3D geometries which are unique to a given vehicle type. We demonstrate this technique using passive, simulated image based sensor measurements and three separate inertial track formulations. We demonstrate the capability to classify the 3D geometries in multiple transform domains (PCA & LDA) using Minimum Euclidean Distance, Maximum Likelihood and Artificial Neural Networks. Additionally, we demonstrate the ability to fuse separate classifiers from multiple domains via Bayesian Networks to achieve ensemble classification

Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp

Electronic warfare self-protection of battlefield helicopters : a holistic view

The dissertation seeks to increase understanding of electronic warfare (EW) self-protection (EWSP) of battlefield helicopters by taking a holistic (systems) view on EWSP. It also evaluates the methodologies used in the research and their suitability as descriptive tools in communication between various EWSP stakeholders. The interpretation of the term "holistic view" is a central theme to the dissertation. The research methodology is bottom-up – which is necessary since no previous work exists that could guide the study – and progresses from analysis to synthesis. Initially several methods are evaluated for presenting findings on EWSP, including high-level system simulation such as Forrester system dynamics (FSD). The analysis is conducted by a comprehensive literature review on EW and other areas that are believed to be of importance to the holistic view. Combat scenarios, intelligence, EW support, validation, training, and delays have major influence on the effectiveness of the EWSP suite; while the initial procurement decision on the EWSP suite sets limits to what can be achieved later. The need for a vast support structure for EWSP means that countries with limited intelligence and other resources become dependent on allies for support; that is, the question of EWSP effectiveness becomes political. The synthesis shows that a holistic view on EWSP of battlefield helicopters cannot be bounded in the temporal or hierarchical (organizational) senses. FSD is found to be helpful as a quality assurance tool, but refinements are needed if FSD is to be useful as a general discussion tool. The area of survivability is found to be the best match for the holistic view – for an EWSP suprasystem. A global survivability paradigm is defined as the ultimate holistic view on EWSP. It is suggested that future research should be top-down and aiming at promoting the global survivability paradigm. The survivability paradigm would give EWSP a natural framework in which its merits can be assessed objectively.reviewe

Masint Fusion Of Multispectral, Hyperspectral &Amp; Kinematic Phenomenology

The evolution of Electro-Optical (EO) technology has continuously advanced towards minimizing a sensor\u27s size, weight and cost while simultaneously increasing key performance metrics such as resolution and range. Frequently, these dichotomous objectives make for a trade space which is difficult to resolve. Fortunately, the use of unresolved multispectral and hyperspectral imagery fused with other Measurement And Signature Intelligence (MASINT) such as target kinematic features presents an opportunity to extend a sensor\u27s useful classification / discrimination range without requiring additional hardware innovation. In this paper, we will provide an overview of multispectral, hyperspectral and kinematic based MASINT phenomenologies which are available for extraction and exploitation. Additionally, we will review feature and information fusion from the perspective of Maximum Likelihood, Naïve Bayes and Bayesian Belief Networks in the context of MASINT Information Fusion

MASINT fusion of multispectral, hyperspectral & kinematic phenomenology

The evolution of Electro-Optical (EO) technology has continuously advanced towards minimizing a sensor\u27s size, weight and cost while simultaneously increasing key performance metrics such as resolution and range. Frequently, these dichotomous objectives make for a trade space which is difficult to resolve. Fortunately, the use of unresolved multispectral and hyperspectral imagery fused with other Measurement And Signature Intelligence (MASINT) such as target kinematic features presents an opportunity to extend a sensor\u27s useful classification / discrimination range without requiring additional hardware innovation. In this paper, we will provide an overview of multispectral, hyperspectral and kinematic based MASINT phenomenologies which are available for extraction and exploitation. Additionally, we will review feature and information fusion from the perspective of Maximum Likelihood, Naïve Bayes and Bayesian Belief Networks in the context of MASINT Information Fusion