With the increasing number of small Uncrewed Aerial Systems (sUAS) in the airspace, the need for robust Detect and Avoid (DAA) technologies is clear. This is especially true when considering the potential for non-cooperative aircraft with unknown intent. Many UAS use high resolution cameras to perform omnidirectional scans of their nearby airspace to localize traffic. These scans can be quite computationally expensive and often necessitate the use of costly and heavy hardware components. Ground-based solutions such as centralized, stationary towers are often expensive, difficult to proliferate, and have the disadvantage of not being onboard the aircraft and as such not always local to the airspace conflict. A feasibility exploration of acoustic detection and localization of non-cooperative aircraft using a low-cost microphone array, computationally inexpensive beamforming algorithms, and filtering techniques, is performed. The cost of the system is minimized by utilizing widely proliferated microphone hardware originally designed for short-range voice detection, as well as a small Uncrewed Aerial Systems (sUAS) from a developmental kit. Lastly, an exploration is conducted to maximize the detection range of the microphone system. A comparison of filtering techniques to try to filter sUAS self-noise is compared to alternative methods such as a ballistic sampling period where the motors of the sUAS are momentarily turned off to reduce noise. A final recommendation of a multi-sensor suite of microphones, cameras, along with other potential sensors, is determined. / Master of Science / As the number of drones increases throughout many industries, safe usage becomes very important. Industries such as search and rescue, infrastructure surveying, package delivery, and more, all have novel uses for drones that could change the way those industries operate. It is easy to imagine the benefit of same-day shipping with package-carrying drones, the quick location of a missing person by a search and rescue drone, and so on. However, obstacles such as buildings, trees, and other air traffic pose an obvious risk. Current methods to detect other aircraft often rely on cameras onboard the aircraft to spot nearby traffic. Other methods include using centralized stations on the ground to relay information about positioning between cooperating aircraft. These technologies provide functionality, but often can be expensive, heavy, require computers with large processing power, or assume the cooperation of the aircraft. An analysis of audio based detection of nearby drones is conducted. The microphones used were originally intended for use in home applications as a voice assistant. Programming techniques were used to listen and identify the sound of a nearby drone. Depending on the location of the drone, its sound would arrive to the microphones in unique time delays, providing a method of estimating the drone's position. Testing was performed on the ground and in the air to analyze the distance at which this microphone group could find a drone. Ultimately, a recommendation for the inclusion of microphones in a suite of sensors was made.

In order to increase the safety of all air travel, technologies that continueto augment the pilot's ability to avoid collisions and stay clear of danger areneeded. But, before these can be certified and deployed, their performance andpotential failure cases have to be understood. This requires evaluating a modelof the system on simulated encounters, consisting of different trajectoriesthat should replicate the real world. This is commonly done using a statistical encounter model, which produces largeamounts of data but relies on the accuracy of the statistical model, thuslimited in its ability to produce realistic data. The goal with this project isto create an encounter dataset of real trajectories that would provide analternative to encounter models. This is done using an ADS-B dataset from The OpenSky Network (provided byDaedalean AI), consisting of 226 billion air traffic data points from 2019.First, a solution to efficiently query and reconstruct trajectories from thedataset is designed and implemented. Using it, a NMAC (Near Mid-Air Collision)dataset is created to demonstrate the viability of ADS-B as a source forcreating an encounter dataset, and to prove the capabilities of the designedsolution.

The current proposed unmanned aircraft system (UAS) detect and avoid standards require the same safety metrics, even when in close proximity to the ground or structures. This requirement has the potential to hinder low altitude small unmanned aircraft operations, such as local package delivery and utility inspection. One of the main safety metrics for UASs to adhere to is a ``well clear" volume that quantifies the vertical and horizontal separation UASs are required to maintain from manned aircraft. The current volume of 2000 feet horizontal and +/- 250 feet vertical does not provide credit for the safety benefit of being close to an obstacle where manned aircraft do not fly and could prove to be too restricting for low-level flight operations (i.e., under 400 feet above ground level). This thesis suggests using smaller safety metric volumes than the well clear volume to demonstrate that operations at lower altitudes can still be proven to be just as safe as if they were held to the larger well clear volume standard by using obstacle and terrain shielding. The research leverages simulation to analyze different safety metrics and provides an example use case in which the methodology of shielded operations is applied to demonstrate how this methodology can be applied for a safety case. / Master of Science / With the development of small unmanned aircraft system (sUAS) technologies have come many practical and regulatory challenges, especially in low altitude airspaces. At lower altitudes, manned aircraft are likely to be operating at lower velocities and restricting standards require UASs to maneuver against aircraft that may not present a significant risk of collision. The excessive avoidance maneuvering can cause the successful execution of even simple operations such as package delivery or survey operations to become difficult. The strict requirements have the potential to specifically inhibit sUAS beyond visual line-of-sight commercial operations, which are of great interest to the industry. This thesis describes a method for demonstrating an equivalent level of safety of small UAS operations when utilizing avoidance algorithms that leverage obstacle and terrain awareness. The purpose of this research is to demonstrate that by remaining close to obstacles, which pose a hazard to other aircraft, an unmanned aircraft can lower the risk of a mid-air collision and to demonstrate an equivalent level of safety for operations using a reduced safety metrics.

Unmanned Aircraft Systems (UAS) face one common challenge when integrating with the existing manned aircraft population in the National Airspace System (NAS). To unlock the full efficiency of UAS, the UAS integrator must comply with an onboard pilot’s requirement to see-and-avoid other aircraft while operating. Commercially available Detect-and-Avoid (DAA) sensor technologies have been developed to attempt to comply with this requirement. UAS integrators must use these sensors to meet or exceed the performance of a human pilot. This thesis covers research done to integrate an array of commercially made DAA sensors with a large Group 3 UAS both in hardware and software that was later flight tested and evaluated for usability. A fast-time simulation is presented using the principles of the National Aeronautics and Space Administration's (NASA) Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS). Last, open-source tools are presented to assist future integrators in validating their DAA solutions.

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.

Robust communication is required to provide a safe airspace for the large numbers of unmanned aerial systems that are coming to the National Airspace System (NAS). This thesis explores methods for providing robust communication to large numbers of vehicles in the NAS. Automatic dependent surveillance-broadcast (ASD-B) is a transmission system that can transmit to and is currently required on all manned aircraft. Unfortunately, ADS-B suffers connectivity problems when supporting large numbers of unmanned aerial systems (UAS). The 5G Cellular protocol can support large numbers of UAS, but connectivity suffers with an increase in distance and interference. Using a 5G cellular and an ADS-B simulator we evaluate the advantages of a combined ADS-B and 5G Cellular transmission system compared to a 5G or ADS-B exclusive system. We also offer hybrid system recommendations that clarify the appropriate operation strategies or triggers that should prompt transitions between transmission systems in different environmental situations. The simulation results show message success and vehicle collision rates, with each messaging method investigated to show the case for a combined communication system. This study shows that a hybrid transmission system is a possible communication solution for UAS operating in beyond visual line of sight (BVLOS) environments.

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.

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.