Unmanned aerial vehicles (UAV's) have recently gained popularity in military, civil service, agriculture, commercial, and hobby use. This is due in part to their affordability, which comes from advances in component technology. That technology includes microelectromechanical systems (MEMS) for inertial sensing, microprocessor technology for sequential algorithm processing, field programmable gate arrays (FPGA's) for parallel data processing, camera technology, global navigation satellite systems (GNSS's) for navigation, and battery technology such as the high energy density of lithium polymer batteries. Despite the success of the technology to date, there remains development before UAV's should be flying alongside manned aircraft or over populated areas. One concern is that UAV electronics are not as safe, reliable or robust as manned-aircraft electronics because UAV's are not certified by the FAA. Another concern for UAV operation is with control algorithms and sensors, particularly in the estimation of the aircraft state, which is the position, velocity, and orientation of the aircraft. Some problems, such as numerical stability of a control algorithm or flight in windy and turbulent conditions have only been solved for certain conditions of wind, weather, or maneuvers. Outside those conditions, the actual orientation of a flying craft can mislead to the control system, and the control system may not be able to recover without a crash. When pilots fly manned aircraft in instrument meteorological conditions, or conditions of limited visibility of the ground, terrain, and obstacles, the pilot must fly in a manner which avoids abrupt maneuvers which could disturb accuracy of the aircraft's instruments. In a UAV without a pilot, there is a need to estimate the position and orientation of a UAV in an absolute manner unambiguous relative to the Earth. The position and orientation estimate must not depend on carefully controlled flight paths, but instead the estimate must be robust in the presence of UAV flight dynamics. This thesis describes the design, implementation, and evaluation of a hardware platform for GPS based orientation sensing research. In this work, we considered a receiver with three or four RF sections, each connected to an antenna in a triangular or tetrahedral pyramid constellation. Specific requirements for the receiver hardware and functionality were created. Circuitry was designed to meet the requirements using commercial off-the-shelf (COTS) radio frequency (RF) modules, a mid-sized microcontroller, an FPGA, and other supporting components. A printed circuit board (PCB) was designed, fabricated, assembled, and tested. A GPS baseband processor was designed and coded in Verilog hardware description language. The design was synthesized and loaded to the FPGA, and the microcontroller was programmed to track satellites. With the hardware platform implemented, live satellite signals were found and tracked, and experiments were performed to explore the validity of GPS based orientation sensing using short antenna baselines. The platform successfully allows the user to develop correlator designs and explore carrier phase based orientation measurement using only software/Verilog modifications. Initial results of carrier phase based orientation sensing are promising, but the presence of multipath signal interference shows room for improvement to the baseband processing code.

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

This work focuses on path planning for an autonomous UAV to tour and cover multiple regions in the shortest time. The three challenges to be solved are - finding the right optimal order to tour the neighborhoods, determining entry and exit points to neighborhoods, and covering each neighborhood. Two approaches have been explored and compared to achieve this goal - a TSP - Greedy and TSP - Dijkstra's. Both of them use a TSP solution to determine the optimal order of touring. They also use the same back and forth motion to cover each region. However, while the first approach uses a brute force to determine the the next closest node of entry or exit, the second approach utilizes the Dijkstra's algorithm to compute all possible paths to every node in the graph, and therefore choose the shortest pairs of entry and exit for each region, that would generate the shorter path, overall. The main contribution of this work is to implement an existing algorithm to combine the touring and covering problem, and propose a new algorithm to perform the same. Empirical results comparing performances of both approaches are included. Hardware experiments are performed on a spraying hexacopter, using the TSP - Greedy approach. Unique system characteristics are studied to make conclusions about stability of the platform. Future directions are identified to improve both software and hardware performance. / Master of Science

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimize the yield of high quality cured leaf. Aerial remote sensing, specifically unmanned aerial vehicles (UAVs), present flue-cured tobacco producers and researchers with a potential tool for scouting and crop management. A two-year study, conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms, assessed the potential of aerial remote sensing in flue-cured tobacco. The effort encompassed two key objectives. First, examine the use of the enhanced normalized difference vegetation index (ENDVI) for separating flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. In 2017, UAV-acquired ENDVI surveys demonstrated the ability to consistently separate between flue-cured tobacco varieties and nitrogen rates from topping to harvest. In 2018, ENDVI revealed significant differences among N-rates as early as 34 days after transplanting. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. Testing of the indices showed significant differences between the index values of healthy and symptomatic plants (alpha = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (alpha = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. / Master of Science / Unmanned Aerial Vehicle’s (UAVs) or drones, as they are commonly referred to, may have potential as a tool in flue-cured tobacco research and production. UAVs combined with sensors and cameras provide the opportunity to gather a large amount of data on a particular crop, which may be useful in crop management. Given the intensive management of flue-cured tobacco, producers may benefit from extra insight on how to better assess threats to yield such as under-fertilization and disease pressure. A two-year study was conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms. There were two objectives to this effort. First, assess the ability of UAV-acquired multispectral near-infrared imagery to separate flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and machine learning models that can accurately predict the incidence of black shank in flue-cured tobacco. Flue-cured tobacco nitrogen rates were significantly different in 2017 from 59 days after transplanting to harvest using UAV-acquired near-infrared imagery. In 2018, heavy rainfall may have led to nitrogen leaching from the soil resulting in nitrogen rates being significantly different as early as 34 days after transplanting. The imagery also showed a significant relationship with variety maturation type in the late stages of crop development during ripening. Two hyperspectral indices were developed and one machine learning model was trained. Each had the ability to detect black shank incidence in fluecured tobacco pre-symptomatically, as well as separated black shank infested plants from healthy plants.

Unmanned Aerial Systems (UAS) are aircraft without a human pilot on board. They are comprised of a ground-based autonomous or human operated control system, an unmanned aerial vehicle (UAV) and a communication, command and control (C3) link between the two systems. UAS are widely used in military warfare, wildfire mapping, aerial photography, etc primarily to collect and process large amounts of data. While they are highly efficient in data collection and processing, they are susceptible to software espionage and data manipulation. This research aims to provide a novel solution to enhance the security of the flight controller thereby contributing to a secure and robust UAS. The proposed solution begins by introducing a new technology in the domain of flight controllers and how it can be leveraged to overcome the limitations of current flight controllers. The idea is to decouple the applications running on the flight controller from the task of data validation. The authenticity of all external data processed by the flight controller can be checked without any additional overheads on the flight controller, allowing it to focus on more important tasks. To achieve this, we introduce an adjacent controller whose sole purpose is to verify the integrity of the sensor data. The controller is designed using minimal resources from the reconfigurable logic of an FPGA. The secondary I/O processor is implemented on an incipient Zynq SoC based flight controller. The soft-core microprocessor running on the configurable logic of the FPGA serves as a first level check on the sensor data coming into the flight controller thereby forming a trusted boundary layer. / Master of Science

The development and implementation of a flight test program for an unmanned aircraft is a multidisciplinary challenge. This thesis presents the development and implementation of a rigorous test program for the flight test of a Geometrically Scaled Joined Wing SensorCraft Remotely Piloted Vehicle from concept through successful flight test. The design methodology utilized in the development of the test program is presented, along with the extensive formal review process required for the approval of the test plan by the Air Force Research Laboratory. The design, development and calibration of a custom instrumentation package is also presented along with the setup, procedure and results from all testing. Results are presented for a wind tunnel test for air data boom calibration, propulsion system static thrust testing, a bifilar pendulum test for experimental calculation of mass moments of inertia, a static structural loading test for structural design validation, a full taxi test and a successful first flight. / Master of Science

This dissertation progressively addresses research problems related to the trajectory optimization for fuel cell powered UAVs, from propulsion system model development, to optimal trajectory analyses and optimal trajectory applications. A dynamic model of a fuel cell powered UAV propulsion system is derived by combining a fuel cell system dynamic model, an electric motor dynamic model, and a propeller performance model. The influence of the fuel cell system dynamics on the optimal trajectories of a fuel cell powered UAV is investigated in two phases. In the first phase, the optimal trajectories of a fuel cell powered configuration and that of a conventional gas powered configuration are compared for point-to-point trajectory optimization problems with different performance index functions. In the second phase, the influence of the fuel cell system parameters on the optimal fuel consumption cost of the minimum fuel point-to-point optimal trajectories is investigated. This dissertation also presents two applications for the minimum fuel point-to-point optimal trajectories of a fuel cell powered UAV: three-dimensional minimum fuel route planning and path generation, and fuel cell system size optimization with respect to a UAV mission.

Thesis (PhD (Electrical and Electronic Engineering))--Stellenbosch University, 2008. / A strategy for the design of an effective, practically feasible, robust, computationally efficient autopilot for three dimensional manoeuvre flight control of Unmanned Aerial Vehicles is presented. The core feature of the strategy is the design of attitude independent inner loop acceleration controllers. With these controllers implemented, the aircraft is reduced to a point mass with a steerable acceleration vector when viewed from an outer loop guidance perspective. Trajectory generation is also simplified with reference trajectories only required to be kinematically feasible. Robustness is achieved through uncertainty encapsulation and disturbance rejection at an acceleration level. The detailed design and associated analysis of the inner loop acceleration controllers is carried out for the case where the airflow incidence angles are small. For this case it is shown that under mild practically feasible conditions the inner loop dynamics decouple and become linear, thereby allowing the derivation of closed form pole placement solutions. Dimensional and normalised non-dimensional time variants of the inner loop controllers are designed and their respective advantages highlighted. Pole placement constraints that arise due to the typically weak non-minimum phase nature of aircraft dynamics are developed. A generic, aircraft independent guidance control algorithm, well suited for use with the inner loop acceleration controllers, is also presented. The guidance algorithm regulates the aircraft about a kinematically feasible reference trajectory. A number of fundamental basis trajectories are presented which are easily linkable to form complex three dimensional manoeuvres. Results from simulations with a number of different aircraft and reference trajectories illustrate the versatility and functionality of the autopilot. Key words: Aircraft control, Autonomous vehicles, UAV flight control, Acceleration control, Aircraft guidance, Trajectory tracking, Manoeuvre flight control.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / This thesis details the development and comparison of two linear control systems that performhover control for a vertical take-off and landing unmanned aerial vehicle. A non-linear mathematical model of the aircraft dynamics is developed. A classical successive loop closure control approach is presented, which applies static gains to the decoupled model around hover. A variable gain approach is presented using optimal control, which linearises the aircraftmodel around its state at fixed time steps. Simulation performance and robustness results are examined for both systems. Different aspects of both controller design processes and results are compared, including navigational performance, robustness and ease of use.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / This thesis describes the successful development of an autopilot for an unmanned radio controlled helicopter. It presents a non-linear helicopter model. An adaptive linearised model is derived and used to design a controller. The adaptive full state controller is tested in various ways, including two aerobatic manoeuvres. A number of analyses are performed on the controller, including its robustness to parameter changes, noisy estimates, wind and processing power. The controller is compared with a non-adaptive counterpart, which leads to the conception, design and analysis of a much improved control structure. Practical flight test results are presented and analysed. In some instances available literature was reworked and re-derived to produce a genericmodel-controller package that can easily be adapted for helicopters of any make, model and size.