Small unmanned aircraft systems (UAS) are rapidly gaining popularity. As the excitement surrounding small UAS has grown, the Federal Aviation Administration (FAA) has repeatedly stated that UAS must be capable of detecting and avoiding manned and unmanned aircraft. In developing detect-and-avoid (DAA) technology, one of the key challenges is identifying a suitable sensor. Automatic Dependent Surveillance-Broadcast (ADS-B) has gained much attention in both the research and consumer sectors as a promising solution. While ADS-B has many positive characteristics, further analysis is necessary to determine if it is suitable as a DAA sensor in environments with high-density small UAS operations. To further the understanding of ADS-B, we present a characterization of ADS-B measurement error that is derived from FAA regulations. Additionally, we analyze ADS-B by examining its strengths and weaknesses from the perspective of DAA on small UAS. To demonstrate the need and method for estimation of ADS-B measurements, we compare four dynamic filters for accuracy and computational speed. The result of the comparison is a recommendation for the best filter for ADS-B estimation. We then demonstrate this filter by estimating ADS-B measurements that have been recorded from the National Airspace System (NAS). We also present a novel long-range, convex optimization-based path planner for ADS-B-equipped small UAS in the presence of intruder aircraft. This optimizer is tested using a twelve-state simulation of the ownship and intruders.We also consider the effectiveness of ADS-B in high-density airspace. To do this we present a novel derivation of the probability of interference for ADS-B based on the number of transmitting aircraft. We then use this probability to document the need for limited transmit range for ADS-B on small UAS. We further leverage the probability of interference for ADS-B, by creating a tool that can be used to analyze self-separation threshold (SST) and well clear (WC) definitions based on ADS-B bandwidth limitations. This tool is then demonstrated by evaluating current SST and WC definitions and making regulations recommendations based on the analysis. Coupling this tool with minimum detection range equations, we make a recommendation for well clear for small UAS in ADS-B congested airspace. Overall these contributions expand the understanding of ADS-B as a DAA sensor, provide viable solutions for known and previously unknown ADS-B challenges, and advance the state of the art for small UAS.

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency.

Modern radar technology relies heavily on digital signal processing. As radar technology pushes the boundaries of miniaturization, computational systems must be developed to support the processing demand. One particular application for small radar technology is in modern drone systems. Many drone applications are currently inhibited by safety concerns of autonomous vehicles navigating shared airspace. Research in radar based Detect and Avoid (DAA) attempts to address these concerns by using radar to detect nearby aircraft and choosing an alternative flight path. Implementation of radar on small Unmanned Air Vehicles (UAV), however, requires a lightweight and power efficient design. Likewise, the radar processing system must also be small and efficient.This thesis presents the design of the processing system for a small Frequency Modulated Continuous Wave (FMCW) phased array radar. The radar and processing is designed to be light-weight and low-power in order to fly onboard a UAV less than 25 kg in weight. The radar algorithms for this design include a parallelized Fast Fourier Transform (FFT), cross correlation, and beamforming. Target detection algorithms are also implemented. All of the computation is performed in real-time on a Xilinx Zynq 7010 System on Chip (SoC) processor utilizing both FPGA and CPU resources.The radar system (excluding antennas) has dimensions of 2.25 x 4 x 1.5 in3, weighs 120 g, and consumes 8 W of power of which the processing system occupies 2.6 W. The processing system performs over 652 million arithmetic operations per second and is capable of performing the full processing in real-time. The radar has also been tested in several scenarios both airborne on small UAVs as well as on the ground. Small UAVs have been detected to ranges of 350 m and larger aircraft up to 800 m. This thesis will describe the radar design architecture, the custom designed radar hardware, the FPGA based processing implementations, and conclude with an evaluation of the system's effectiveness and performance.

The master’s thesis expresses an opinion on trends in UAV integration into non-segregated airspace issue. In the beginning, barriers to integration are characterized and a broader context is shown. Since necessity of the technical realization of the “see and be seen” principle exists, requirements for so called Sense and Avoid systems are presented. Various methods of Sense and Avoid are briefly described, highlighting their contribution to air safety and their potential for future development. The UAV communication topic is described in the last chapter, providing a list of the volume of transmitted messages, analyzing data link frequencies and selecting appropriate means of UAV operations.

Modern radar technology relies heavily on digital signal processing. As radar technology pushes the boundaries of miniaturization, computational systems must be developed to support the processing demand. One particular application for small radar technology is in modern drone systems. Many drone applications are currently inhibited by safety concerns of autonomous vehicles navigating shared airspace. Research in radar based Detect and Avoid (DAA) attempts to address these concerns by using radar to detect nearby aircraft and choosing an alternative flight path. Implementation of radar on small Unmanned Air Vehicles (UAV), however, requires a lightweight and power efficient design. Likewise, the radar processing system must also be small and efficient. This thesis presents the design of the processing system for a small Frequency Modulated Continuous Wave (FMCW) phased array radar. The radar and processing is designed to be light-weight and low-power in order to fly onboard a UAV less than 25 kg in weight. The radar algorithms for this design include a parallelized Fast Fourier Transform (FFT), cross correlation, and beamforming. Target detection algorithms are also implemented. All of the computation is performed in real-time on a Xilinx Zynq 7010 System on Chip (SoC) processor utilizing both FPGA and CPU resources. The radar system (excluding antennas) has dimensions of 2.25 x 4 x 1.5 in3, weighs 120 g, and consumes 8 W of power of which the processing system occupies 2.6 W. The processing system performs over 652 million arithmetic operations per second and is capable of performing the full processing in real-time. The radar has also been tested in several scenarios both airborne on small UAVs as well as on the ground. Small UAVs have been detected to ranges of 350 m and larger aircraft up to 800 m. This thesis will describe the radar design architecture, the custom designed radar hardware, the FPGA based processing implementations, and conclude with an evaluation of the system's effectiveness and performance.

For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second, geometric velocity vectors (GVV), assumes an instantaneous bank-angle maneuver. The solution using the first method must be found numerically, while the second has a closed-form analytical solution. These methods are compared to two existing methods. Results show the time-based geometric velocity vectors approach is precise, and the geometric velocity vectors approach is a good approximation under many conditions. The DAA problem requires the use of a robust target detection and tracking algorithm for tracking multiple maneuvering aircraft in the presence of noisy, cluttered, and missed measurements. Additionally these algorithms needs to be able to detect overtaking intruders, which has been resolved by using multiple radar sensors around the aircraft. To achieve these goals the formulation of a nonlinear extension to R-RANSAC has been performed, known as extended recursive-RANSAC (ER-RANSAC). The primary modifications needed for this ER-RANSAC implementation include the use of an EKF, nonlinear inlier functions, and the Gauss-Newton method for model hypothesis and generation. A fully functional DAA system includes target detection and tracking, collision detection, and collision avoidance. In this research we demonstrate the integration of each of the DAA-system subcomponents into fully functional simulation and hardware implementations using a ground-based radar setup. This integration resulted in various modifications of the radar DSP, collision detection, and collision avoidance algorithms, to improve the performance of the fully integrated DAA system. Using these subcomponents we present flight results of a complete ground-based radar DAA system, using actual radar hardware.

The increasing demand to integrate unmanned aircraft systems (UAS) into the national airspace is motivated by the rapid growth of the UAS industry, especially small UAS weighing less than 55 pounds. Their use however has been limited by the Federal Aviation Administration regulations due to collision risk they pose, safety and regulatory concerns. Therefore, before civil aviation authorities can approve routine UAS flight operations, UAS must be equipped with sense-and-avoid technology comparable to the see-and-avoid requirements for manned aircraft. The sense-and-avoid problem includes several important aspects including regulatory and system-level requirements, design specifications and performance standards, intruder detecting and tracking, collision risk assessment, and finally path planning and collision avoidance. In this dissertation, our primary focus is on developing an collision detection, risk assessment and avoidance framework that is computationally affordable and suitable to run on-board small UAS. To begin with, we address the minimum sensing range for the sense-and-avoid (SAA) system. We present an approximate close form analytical solution to compute the minimum sensing range to safely avoid an imminent collision. The approach is then demonstrated using a radar sensor prototype that achieves the required minimum sensing range. In the area of collision risk assessment and collision prediction, we present two approaches to estimate the collision risk of an encounter scenario. The first is a deterministic approach similar to those been developed for Traffic Alert and Collision Avoidance (TCAS) in manned aviation. We extend the approach to account for uncertainties of state estimates by deriving an analytic expression to propagate the error variance using Taylor series approximation. To address unanticipated intruders maneuvers, we propose an innovative probabilistic approach to quantify likely intruder trajectories and estimate the probability of collision risk using the uncorrelated encounter model (UEM) developed by MIT Lincoln Laboratory. We evaluate the proposed approach using Monte Carlo simulations and compare the performance with linearly extrapolated collision detection logic. For the path planning and collision avoidance part, we present multiple reactive path planning algorithms. We first propose a collision avoidance algorithm based on a simulated chain that responds to a virtual force field produced by encountering intruders. The key feature of the proposed approach is to model the future motion of both the intruder and the ownship using a chain of waypoints that are equally spaced in time. This timing information is used to continuously re-plan paths that minimize the probability of collision. Second, we present an innovative collision avoidance logic using an ownship centered coordinate system. The technique builds a graph in the local-level frame and uses the Dijkstra's algorithm to find the least cost path. An advantage of this approach is that collision avoidance is inherently a local phenomenon and can be more naturally represented in the local coordinates than the global coordinates. Finally, we propose a two step path planner for ground-based SAA systems. In the first step, an initial suboptimal path is generated using A* search. In the second step, using the A* solution as an initial condition, a chain of unit masses connected by springs and dampers evolves in a simulated force field. The chain is described by a set of ordinary differential equations that is driven by virtual forces to find the steady-state equilibrium. The simulation results show that the proposed approach produces collision-free plans while minimizing the path length. To move towards a deployable system, we apply collision detection and avoidance techniques to a variety of simulation and sensor modalities including camera, radar and ADS-B along with suitable tracking schemes.

Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. These UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This research presents an obstacle avoidance system for small UAVs. First, a mission waypoint path is created that avoids all known obstacles using a genetic algorithm. Then, while the UAV is in flight, obstacles are detected using a forward looking, onboard camera. Image features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially hazardous areas. The small UAV then employs a sliding mode control law on the autopilot to avoid obstacles. This research compares rapidly-exploring random trees to genetic algorithms for UAV pre-mission path planning. It also presents two methods for using image feature movement and UAV telemetry to calculate depth and detect obstacles. The first method uses pixel ray intersection and the second calculates depth from image feature movement. Obstacles are avoided with a success rate of 96%.

A simple and novel dual-CP printed antenna is modelled and measured. The patch antennais small and achieves a low axial ratio without quadrature feeding. The measured pattern showsaxial ratio pattern squinting over frequency. Possible methods of improving the individual element are discussed, as well as an array technique for improving the axial ratio bandwidth. Three endfire printed antenna structures are designed, analyzed, and compared. The comparison includes an analysis of costs of production for the antenna structures in addition to their performance parameters. This analysis concludes that cost of materials primarily reduces the size of antennas for a given gain and bandwidth. An antenna stucture with an annular beam pattern for down-looking navigational radar is proposed. The antenna uses sub-wavelength grating techniques from optics to achieve a highly directive planar reflector which is used as a ground plane for a monopole. A fan-beam array element is fabricated for use in a digitally steered receive array for obstacle avoidance radar. The steered beam pattern is observed. The element-dependent phase shifts for a homodyned signal in particular are explored as to their impact on beam steering.