171-180 of 237 results
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An Empirical Study of the Evolution of Homeland Security Definitions in Federal Documents
PI Alexander Siedschlag
CO-I Andrea Jerkovic
This ongoing empirical study (quantitative systematic review) systematically tracks the evolution of official homeland security definitions from related federal strategies, frameworks, guidelines, the Quadrennial Homeland Security Reviews (QHSRs), enterprise agencies’ strategic plans, and pertinent legislation. Continuing and changing ingredients of the sprouting public policy and strategy definition of homeland security will be identified.
Twenty years after 9/11, the field of homeland security has benefited from several conceptual studies. Those assessed and made recommendations on main domains of homeland security from scholastic and normative viewpoints, how the concept of homeland security should be mirrored in curriculum development, evolution, and program learning outcomes, and what competencies a homeland security graduate, scholar, and educated practitioner needs. However, few studies and texbooks address the evolution of the term of homeland security in the homeland security era from 9/11 to now. While valuable analyses of how homeland security predates and transcends 9/11 have increased, systematic study of how the concept and meaning of homeland security have evolved over the past 20 years continues to be scarce. This ongiong empirical study (quantitative systematic review) systematically tracks the evolution of official homeland security definitions from related federal strategies, frameworks, guidelines, the Quadrennial Homeland Security Reviews (QHSRs), enterprise agencies’ strategic plans, and pertinent legislation. Continuing and changing ingredients of the sprouting public policy and strategy definition of homeland security will be identified.Categories: Faculty-Staff
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ICARUS Drone Net
PI Samuel Siewert
CO-I Iacopo Gentilini
CO-I Mehran Andalibi
CO-I Stephen Bruder
Drone Net is a conceptual architecture to integrate passive sensor nodes in a local sensor network along with traditional active sensing methods for small Unmanned Aerial Systems traffic management. The goal of the proposed research architecture is to evaluate use of multiple passive sensor nodes integrating Electro-Optical/Infrared and acoustic arrays networked around a UAS Traffic Management operating region (Class G uncontrolled airspace). The Drone Net approach will be compared to and/or used in addition to RADAR and Automatic Dependent Surveillance-Broadcast tracking and identification. We hypothesize that this approach can better manage non-compliant small UAS along with compliant UAS and general aviation in sensitive airspace, urban locations, and geo-fenced regions.
Drone-Net: Information Fusion Networks for Reliable Localization
ICARUS Research Group: ERAU Prescott
The challenge and opportunity presented by use of UAS “drones” in the national airspace (NAS) has historic significance not seen since the early days of aviation growth after the First World War. The FAA estimates that by 2020 the drone market will be $98 billion with 7 million drones added annually [1]. Market beneficiaries include industrial inspection, aerial photography, insurance, agricultural and government services [1]. While ADS-B for drones, along with registration, has been proposed as a quick fix, to allow drones into the NAS and to share populated areas, it is not clear how this will work for all types of drones ranging from professional service to hobby. For example, many drones will be fully compliant, but some may be semi-compliant (e.g., low quality position reporting due to degraded GPS), and others perhaps even totally non-compliant or hostile [2].
Embryonic research at ERAU Prescott to develop a drone detector, which can be placed on roof-tops and networked with other detectors and information services, has shown that multi-spectral EO/IR detection is quite effective. The feasibility of passive methods for civil aviation detection of aircraft not using ADS-B and not registered on flilghtradar24 [3] (via primary and secondary RADAR) has been experimentally observed.
The ICARUS group at ERAU Prescott is working to fly and test ADS-B on drones [9] that are compliant, semi-compliant, and non-compliant in order to evaluate methods of detection, classification and identification. Research by Sandia National Labs has shown that drones typically have very low RADAR cross-section area, similar to stealth aircraft [4] and can present a significant security and safety threat. The risk is that even one or two national security incidents involving service drones, hobby, or terrorism could result in the grounding of all drones in the NAS.
Multiple studies [4, 5] have substantiated the conclusion that no single sensing modality will suffice to reliably detect and localize a wide variety of drones. To this end, the ICARUS group proposes to pursue a heterogeneous information fusion approach with passive EO/IR and progressing to a “richer” passive/active sensor suite. Prior ICARUS research partially funded by DHS ADAC Center of Excellence (CoE) led to development of the SMDSI (Software Defined Multi-Spectral Imager) to detect and track marine traffic [6, 7]. This existing hardware will be adapted to accommodate additional sensors including acoustic, ADS-B, primary/secondary RADAR, and LIDAR in order to accelerate the development of optimal methods of drone detection, classification, and identification.
The first year of this proposed effort, will involve basic research in machine learning, machine vision, real-time systems, and the development of suitable information fusion algorithms, continuing through the duration. The test configuration will be documented to facilitate replication at other participating academic research organizations (ERAU Daytona ASSURE [11], U. of Alaska ACUASI [12], U. of Alaska DHS ADAC, and U. of Colorado Boulder Embedded Systems Engineering) in year two of the proposed research. Each collaborator can provide unique synergy to enhance research objectives and are potential future external funding partners. The team will include ERAU Prescott faculty, undergraduate students, CU Boulder graduate ESE students, and collaboration with faculty at U. of Alaska and Colorado. In the third year of work, passive capabilities can be enhanced with active LIDAR and existing RADAR at ERAU Prescott. The goal is to develop unique sensor fusion algorithms as well as machine learning and traditional salient object detection and classification methods.
The images collected over the lifetime of the project can further be saved in a distributed database of observed compliant, compliant-low-quality, non-compliant and perhaps hostile drones and shared between “Drone net” nodes. The overall vision is to create a network of passive/active drone detection, classification, and identification nodes to enhance security and safety for drone operations that surpasses ADS-B and registration alone [10]. The capability can be used to test and evaluate commercial drone detection systems being evaluated by the FBI at airports [8] and to make policy and technology recommendations to key agencies (FAA, DOT, DHS) for drone integration into the national air space. Drone net will enable ICARUS to pursue external funding from DHS CoEs, ONR (Office of Naval Research), NASA AIST (Advanced Information Systems Technology), FAA ASSURE, SBIR/STTR, and industry.
References
[1] FAA Aerospace Forecast, Fiscal Years 2016-2036, Federal Aviation Administration.
[2] How consumer drones wind up in the hands of ISIS fighters, Techcrunch, October 13, 2016.
[3] flightradar24.com, ADS-B, primary/secondary RADAR flight localization and aggregation services.
[4] Birch, Gabriel Carisle, John Clark Griffin, and Matthew Kelly Erdman. UAS Detection Classification and Neutralization: Market Survey 2015. No. SAND2015-6365. Sandia National Laboratories (SNL-NM), Albuquerque, NM, 2015.
[5] McNeal, Gregory S. "Unmanned Aerial System Threats: Exploring Security Implications and Mitigation Technologies." Available at SSRN (2015).
[6] Arctic Domain Awareness Research Center, U. of Alaska Anchorage, Theme 3, Task 2, SmartCam.
[7] S. Siewert, M. Vis, R. Claus, R. Krishnamurthy, S. B. Singh, A. K. Singh, S. Gunasekaran, “Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks”, (in preparation) AIAA SciTech, Grapevine, Texas, January 2017.
[8] FAA Tests FBI Drone Detection System at JFK, Federal Aviation Administration, July, 2016.
[9] Micro-Avionix, Ping2020 ADS-B transponder for UAS.
[10] Rhode & Schwarz, Protecting the Sky - Signal Monitoring of Radio Controlled Civilian Unmanned Aerial Vehicles and Possible Countermeasures, October 2015.
[11] ACUASI, Alaska Center for Unmanned Aircraft Systems Integration, http://acuasi.alaska.edu/, Dr. Mike Hatfield.
[12] ASSURE, Alliance of System Safety for UAS through Research Excellence, http://assureuas.erau.edu/, Dr. Richard Stansbury.
Categories: Faculty-Staff
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Comparison of Classic Guerrilla Warfare With So-Called Fourth-Generation Warfare Using Agent-Based Modeling and Simulation
PI Jerry Sink
CO-I Mark Abdollahian
Fourth-Generation Warfare (4GW) theory shares many characteristics of classical guerrilla theory (CGW) in security studies literature. Proponents claim that 4GW is a significant evolution that overturns traditional measures of military power, while critics counter that 4GW is simply CGW in an updated context. The two strategies are modeled in an agent-based simulation to evaluate similarities and differences in speed to victory and territory controlled over. Emergent behaviors are compared with historical data.
So-called Fourth-Generation Warfare (4GW) as described by numerous military scholars shares many characteristics of guerrilla tactics in the classical literature, as described by SunTzu, Wellington, Clausewitz, Mao, and Giap. Proponents of 4GW claim that its development has significantly altered the ratio of strength of industrialized and guerrilla forces, and thus the likelihood of "weaker" forces (as measured in previous military contexts) prevailing against forces assessed by traditional measures as stronger. Critics point to a lack of intellectual rigor in defining the salient characteristics of 4GW, and charge that it is simply a re-statement of classical guerrilla war (CGW) tactics, albeit with improved communications and propaganda capabilities, along with a social media cultural context.
This project, which is the topic of the forthcoming PhD dissertation of the author, models CGW and 4GW in an agent-based simulation using NetLogo software in order to explore the differences in time to victory and increased area of territory controlled of CGW and 4GW forces against their respective industrialized and information-age conventional opponents. Expected results include emergent behaviors that offer insights into the similarities and differences of CGW. These are compared to historical data to determine if 4GW is indeed a significant military evolution that threatens to upend traditional measures of military superiority, or if it is merely an adaptation of old tactics to a new context.
Categories: Faculty-Staff
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Astroparticle Physics
PI Darrel Smith
CO-I Brennan Hughey
In the 1950s and 1960s, high-energy and cosmic-ray physics developed into two different fields of research. However, in the last 20 years, they have come together in a most peculiar way. As space physicists explored the sources and mechanisms for producing cosmic rays, they also realized that it was impossible to measure the dynamics of the early universe (i.e., the first 400,000 years).
It is here that particle physics provides a laboratory environment to study the physical processes that occurred in the early universe, a region that cannot be explored directly with the tools of astrophysics. Particle physicists continue to build accelerators with increasing energy densities that simulate the early universe at times less than a microsecond after the "Big Bang." This area of research will investigate how particle physics and astrophysics combine to give us a consistent view of the early universe.Categories: Faculty-Staff
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Exotic Propulsion
PI Darrel Smith
Exotic propulsion has captured the interest of many Embry-Riddle students. As NASA plans its manned mission to Mars, we come face-to-face with a fundamental dilemma — a round trip to Mars will take almost three years with traditional chemical rockets!
Such a journey would be impossible, as it would require the astronauts to live on Mars for almost a year. Furthermore, the long travel time would expose astronauts to lethal doses of radiation and debilitating periods of weightlessness. For the past 30 years, physicists and engineers have been developing exotic propulsion systems with the expectation of reducing the travel time from years down to months. Exotic propulsion systems under current investigation include plasma engines, matter-antimatter engines and nuclear-powered engines.Categories: Faculty-Staff
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Machine Learning for Dynamic Airspace Configuration towards Optimized Mobility in Emergency Situations
PI Houbing Song
Categories: Faculty-Staff
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NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning
PI Houbing Song
CO-I Richard Stansbury
Embry-Riddle Aeronautical University establishes a new Research Experiences for Undergraduates (REU) Site to engage participants in research in drone swarms. The emerging concept of drone swarms, which is defined as the ability of drones to autonomously make decisions based on shared information, creates new opportunities with major societal implications. However, future drone swarm applications and services pose new networking challenges. A resurgence of Artificial Intelligence and machine learning research presents a tremendous opportunity for addressing these networking challenges. There is an overwhelming need to foster a robust workforce with competencies to enable future drone swarm applications and services in the age of AI/machine learning.
The project establishes a new Research Experiences for Undergraduates (REU) Site with a focus on networking research for drone swarms in the age of AI/machine learning at Embry-Riddle Aeronautical University. The goals of the REU Site are: (1) attract undergraduate students to state-of-the-art drone swarm research, especially those from underrepresented groups, and from institutions with limited opportunities; (2) develop the research capacity of participants by guiding them to perform research on drone swarms; (3) grow the participants’ technical skills to enable a wide variety of beneficial applications of drone swarms; (4) promote the participants’ integrated AI/machine learning and drone swarm competencies; and (5) prepare participants with professional skills for careers. The focus of the REU Site is on the design, analysis and evaluation of innovative computing and networking technologies for future drone swarm applications and services. To be specific, research activities will be conducted in three focus areas, notably dynamic network management, network protocol design, and operationalizing AI/machine learning for drone swarms. Each year eight undergraduate students will participate in a ten-week summer REU program to perform networking research for drone swarms under the guidance of research mentors with rich experiences in AI/machine learning and drone swarms. This REU site is expected to foster workforce knowledge and skills about developing new computing and networking technologies for future drone swarm applications and services. This site is supported by the Department of Defense ASSURE program in partnership with the NSF REU program.
Categories: Faculty-Staff
171-180 of 237 results