COUNTER-UXS ENERGY AND OPERATIONAL ANALYSIS

At present, there exists a prioritization of identifying novel and innovative approaches to managing the small Unmanned Aircraft Systems (sUAS) threat. The near-future sUAS threat to U.S. forces and infrastructure indicates that current Counter-UAS (C-UAS) capabilities and tactics, techniques, and procedures (TTPs) need to evolve to pace the threat. An alternative approach utilizes a networked squadron of unmanned aerial vehicles (UAVs) designed for sUAS threat interdiction. This approach leverages high performance and Size, Weight, and Power (SWaP) conformance to create less expensive, but more capable, C-UAS devices to augment existing capabilities. This capstone report documents efforts to develop C-UAS technologies to reduce energy consumption and collaterally disruptive signal footprint while maintaining operational effectiveness. This project utilized Model Based System Engineering (MBSE) techniques to explore and assess these technologies within a mission context. A Concept of Operations was developed to provide the C-UAS Operational Concept. Operational analysis led to development of operational scenarios to define the System of Systems (SoS) concept, operating conditions, and required system capabilities. Resource architecture was developed to define the functional behaviors and system performance characteristics for C-UAS technologies. Lastly, a modeling and simulation (M&S) tool was developed to evaluate mission scenarios for C-UAS.Outstanding ThesisCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Unmanned Aerial Systems: Research, Development, Education & Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast-growing industry is anticipating and responding to a myriad of societal applications that will provide new and more cost-effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation-related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities, and is divided into two parts. The first part provides a brief summary of each of the various activities, while the second part lists the faculty associated with those activities. Within the first part of this document we have separated UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach-D, Prescott-P and Worldwide-W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. If more information is desired, please contact me directly, or visit our research website (https://erau.edu/research), or contact the appropriate faculty member using their e-mail address provided at the end of this document

Unmanned Aerial Systems Research, Development, Education and Training at Embry-Riddle Aeronautical University

With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors and, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast growing industry is anticipating and responding to a myriad of societal applications that will provide either new or more cost effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks. Embry-Riddle Aeronautical University has a long history of aviation related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities. The document is divided into two parts. The first part provides a brief summary of each of the various activities while the second part lists the faculty associated with those activities. Within the first part of this document we have separated the UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach---D, Prescott---P and Worldwide--W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced. We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. Should more information be desired, please contact me directly or alternatively visit our research web pages (http://research.erau.edu) and contact the appropriate faculty member directly

Using Agent-Based Modeling to Evaluate UAS Behaviors in a Target-Rich Environment

The trade-off between accuracy and speed is a re-occurring dilemma in many facets of military performance evaluation. This is an especially important issue in the world of ISR. One of the most progressive areas of ISR capabilities has been the utilization of Unmanned Aircraft Systems (UAS). Many people believe that the future of UAS lies in smaller vehicles flying in swarms. We use the agent-based System Effectiveness and Analysis Simulation (SEAS) to create a simulation environment where different configurations of UAS vehicles can process targets and provide output that allows us to gain insight into the benefits and drawbacks of each configuration. Our evaluation on the performance of the different configurations is based on probability of correct identification, average time to identify a target after it has deployed in the area of interest, and average time to identify all targets in an area

Mission-Oriented Autonomy for Intelligent, Adaptive, and Multi-Agent Remote Sensing of Ice Sheets using Unmanned Aerial Systems

Throughout our history, humanity has been developing and progressing technology in order to help us better understand the world in which we live. As climate change becomes an increasingly urgent global crisis, scientists have been tasked with developing models for better understanding the complex dynamics involved, as well as to more accurately forecast the long term effects on our environment. With respect to sea level rise, both our knowledge of these dynamics and the accuracy of these models can be improved through the routine collection of crucial data concerning glacier ice thickness and bedrock topology. To accomplish this, innovative solutions are being developed by groups of inter-disciplinary research teams, combining fields such as earth-science, radar systems, data science, and aerospace engineering. Through this collaboration, we have the potential to leverage breakthroughs in unmanned systems technology and miniaturized, specialized sensors for comprehensive, precise, and routine data collection of key polar research objectives. As Unmanned Aerial Systems (UASs) have become more reliable research platforms in recent years, they now have the capability to perform these remote sensing operations at a reduced cost compared to manned operations, while also providing repeatable, precision tracking capabilities along flight lines, enabling the surveying of tightly-spaced grids, and removing human flight crews from hazardous polar environments. However, the payload, range, and wind constraints for these platforms severely restrict their operational sensing footprint. Additionally, UASs generally have a much smaller wingspan compared to manned aircraft typically used in Earth Science missions, which becomes a challenging factor for incorporating efficient directive antennas at the low operating frequencies required for glacial sounding. The aim of this work is to address these issues and to enhance mission efficiency and the overall quality of data collection for these operations through the implementation of onboard mission-oriented autonomy that includes cognitive decision-making for intelligent survey operations, adaptive functionalities, and a scalable, robust framework for multi-agent operations. As opposed to conventional methods for polar research operations which generally involve single-agent missions, using standard waypoint guidance and fixed-routes planned by human operators, the unique contributions of the developed mission-oriented autonomy in this work include: 1) Automated flight line generation for rapid and reliable mission planning of tightly-spaced flight lines required for cross-track synthetic aperture radar processes and surface clutter suppression, with required spacing based on the operating frequency of the onboard radar system. 2) Implementation of Dubins Path guidance methods into polar research operations for precision end-to-end survey of mission flight lines while taking into account the kinematic constraints of the fixed wing aircraft, as well as for efficiently traversing to and from a home loiter location during mission operations. 3) Cognitive, real-time optimal path planning through mission flight lines utilizing both deterministic and stochastic Traveling Salesman Problem heuristics. 4) Modifications to these Traveling Salesman Problem heuristics for ensuring safe, feasible, and reliable operations in real-time by taking into account aircraft range constraints. 5) Collaborative Multi-Agent survey operations utilizing space partitioning and Hungarian Assignment for distributed task allocation, as well as morphing potential fields for collision avoidance. 6) Modifications for Multi-Agent deployment scheduling to reduce inter-agent interference for sensitive radar systems to improve coherency of the collected data, and to rapidly and efficiently deploy agents into and out of survey areas. 7) Modifications for Heterogeneous flight operations for increasing operational capabilities through cross-platform collaboration. 8) Failsafe features to instill robustness in Multi-Agent operations with respect towards accommodating and adapting to single-agent system failures, by automatically re-planning collaborative survey operations. In this work, the motivation for the creation of this mission-oriented autonomy is discussed, along with the methodology of each of the autonomy features, and the framework for implementation onto UAS platforms. Case studies are conducted for past and future polar research deployments using unmanned systems to assess the potential improvements in operational capabilities and data collection for the developed autonomy compared to conventional methods. Finally, the developed autonomy is implemented onto an embedded system for preliminary flight testing and validation, as well as used for intelligent mission planning for a manned operation

Threat modeling for communication security of IoT-enabled digital logistics

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure

Sim-to-Real Reinforcement Learning Framework for Autonomous Aerial Leaf Sampling

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS applications is challenging due to the risk of crashes. UAS crashes pose safety risks to the operator and its surroundings which often leads to expensive UAS repairs. In this thesis, we present a Sim-to-Real Transfer framework using a computer numerical control (CNC) platform as a safer, and more robust proxy, before using the controller on a UAS. In addition, our framework provides an end-to-end complete pipeline to learn, and test, any deep RL controller for UAS or any three-axis robot for various control tasks. Our framework facilitates bi-directional iterative improvements to the simulation environment and real robot, by allowing instant deployment of the simulation learned controller to the real robot for performance verification and issue identification. Our results show that we can perform a zero-shot transfer of the RL agent, which is trained in simulation, to real CNC. The accuracy and precision do not meet the requirement for complex leaf sampling tasks yet. However, the RL agent trained for a static target following still follows or attempts to follow more dynamic and changing targets with predictable performance. This works lays the foundation by setting up the initial validation requirements for the leaf sampling tasks and identifies potential areas for improvement. Further tuning of the system and experimentation of the RL agent type would pave the way to autonomous aerial leaf sampling. Adviser: Carrick Detweile

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection