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.

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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.

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.

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 aim of this thesis is to analyse the directors disqualification provisions (SS.63-67 BCA) and wrongful trading provisions (S.68 BCA) and to provide their comparison in the light of British model regulation. First chapter is introductory and provides a brief introduction into the topic. Second chapter deals with the law & economics aspects of the regulation. More specifically it introduces various stakeholders in company and their interests and incentives. Third chapter focuses on the relationship between the director and the company. Further attention is paid to the managerial contract, the duty to avoid insolvent liquidation, the duty of care and the business judgement rule. Fourth chapter deals with the director's liability towards the company. Fifth chapter relates to the directors disqualification. In this chapter grounds for disqualification are dealt with as well as temporal, personal and territorial reach of the directors disqualification. Further attention is paid to the consequences of disqualification and consequences of a breach of the disqualification order. Sixth chapter looks at Company Directors Disqualification Act and finds similarities and differences in both regulations. Seventh chapter looks at the wrongful trading provisions contained in the BCA. Adequate attention is...

As small unmanned aerial systems (UAS) are introduced into the national airspace, measures must be introduced to ensure that they do not interfere with manned aviation and other UAS. Radar provides an attractive solution because of its inherent range accuracy and because it works in diverse weather and lighting conditions. Traditional radar systems, however, are large and high power and do not meet the size, weight and power (SWaP) constraints imposed by UAS, and fully integrated automotive solution do not provide the necessary range. This thesis proposes a compact radar system that meets both the SWaP and range requirements for UAS and can act as a standalone sensor for a sense and avoid system (SAA). The system meets the field of view requirements motivated by the UAS sensing problem (120deg x 30deg) and tracks targets in range and azimuthal angle using a four element phased array receiver. The phased array receiver implements real time correlation and beamforming using a field programmable gate array (FPGA) and can track multiple targets simultaneously. Excluding antennas, the radar transceiver and signal processing platform weighs approximately 120g and is approximately the size of a whiteboard eraser (2.25in x 4in x 1in), which meets the payload requirements of many small (<25kg) UAS. To our knowledge, this is the first real time phased array radar that meets the sensing and SWaP requirements for small UAS.Our testing was done with the radar system on the ground, aimed at airborne UAS targets. Using antennas with a gain of 12 dB, and 800 milliwatts of transmitted power, the system detects UAS targets with a radar cross section of less than 0.1 square meters up to 150 meters away. The ground based system demonstrates radar detectability of extremely small UAS targets, and is scalable to further ranges by increasing antenna gain or adding additional elements. Based on our success in detecting airborne UAS, we conclude that radar remains a feasible option for a UAS collision avoidance sensor.

<p>This thesis investigates the crucial problem of collision avoidance for autonomous vehicles.  An anti-collision system for an unmanned aerial vehicle (UAV) is studied in particular. The purpose of this system is to make sure that the own vehicle avoids collision with other aircraft in mid-air. The sensor used to track any possible threat is for a UAV limited basically to a digital video camera. This sensor can only measure the direction to an intruding vehicle, not the range, and is therefore denoted an angle-only sensor. To estimate the position and velocity of the intruder a tracking system, based on an extended Kalman filter, is used. State estimates supplied by this system are very uncertain due to the difficulties of angle-only tracking. Probabilistic methods are therefore required for risk calculation. The risk assessment module is one of the essential parts of the collision avoidance system and has the purpose of continuously evaluating the risk for collision. To do this in a probabilistic way, it is necessary to assume a probability distribution for the tracking system output. A common approach is to assume normality, more out of habit than on actual grounds. This thesis investigates the normality assumption, and it is found that the tracking output rapidly converge towards a good normal distribution approximation. The thesis furthermore investigates the actual risk assessment module to find out how the collision risk should be determined. The traditional way to do this is to focus on a critical time point (time of closest point of approach, time of maximum collision risk etc.). A recently proposed alternative is to evaluate the risk over a horizon of time. The difference between these two concepts is evaluated. An approximate computational method for integrated risk, suitable for real-time implementations, is also validated. It is shown that the risk seen over a horizon of time is much more robust to estimation accuracy than the risk from a critical time point. The integrated risk also gives a more intuitively correct result, which makes it possible to implement the risk assessment module with a direct connection to specified aviation safety rules.</p>

<p>I en obemannad flygfarkost måste datorer ta över pilotens förmåga att värdera risker och undvika kollisioner. På algoritmnivå brukar man dela in problemet i tre delar: Upptäckt och estimering av inblandade farkosters positioner och hastigheter, kollisionsriskberäkning och slutligen undanmanöver.</p><p>Saabs arbete med obemannade farkoster har tidigare berört kollisionsundvikning lite ytligt men nu börjat på större allvar. Det här examensarbetet är en del i denna satsning och har resulterat i ett sätt att beräkna kollisionsrisken samt ett sätt att beräkna en undanmanöver, givet att de inblandade farkosternas positioner och hastigheter är kända. </p><p>I examensarbetet behandlas parvisa kollisionsscenarier mellan ickekommunicerande farkoster givet två olika fall. Dels där den främmande farkostens position skattats väl, dels där den främmande farkostens position skattats sämre. En enkel simuleringsmiljö har utvecklats, där två algoritmer för beräknandet av kollisionsrisken, en för varje fall, testats samtidigt som undanmanövern testats för en mängd kollisionsscenarier. Givet att den främmande farkostens position skattats väl behöver den obemannade farkosten cirka 6 s på sig för att kunna undvika en kollision. I fallet där den främmande farkostens position skattats sämre kan vi beräkna kollisionsrisken och i vissa fall sluta oss till hur farkosterna är orienterade och därigenom göra ett undanmanöverval.</p> / <p>Saabs work with unmanned aerial vehicles has only scratched the surface of collision avoidance, but is now advancing. This master thesis sheds light on some parts of the collision avoidance problem and has resulted in an innovative way to calculate the risk of collision and a way to determine an avoidance maneuver.</p><p>In this master thesis collision scenarios between non-communicating vehicles are being looked upon in pairs, given two different sets of data. Good estimates of the unknown vehicles position and unsatisfying position estimate. Through the development of a simple simulation environment, two algorithms, one for each set of data, has been tested simultaneously with tests of the collision avoidance maneuver for several collision scenarios. Given a good estimate of the unknown aerial vehicles position, the unmanned vehicle need approximately 6 seconds to act to avoid a collision. For the case with unsatisfactory estimate of the unknown vehicle the risk of collision can be calculated and in some cases the orientation of the aerial vehicles and thus a choice of avoidance maneuver can be made.</p>