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.

Emerging health concern for gaseous nitrogen oxides (NOx) emitted during surface mine blasting has prompted mining authorities in the United States to pursue new regulations. NOx is comprised of various binary compounds of nitrogen and oxygen. Nitric oxide (NO) and nitrogen dioxide (NO2) are the most prominent. Modern explosive formulations are not designed to produce NOx during properly-sustained detonations, and researchers have identified several causes through laboratory experiments; however, direct sampling of NOx following full-scale surface mine blasting has not been accomplished. The purpose of this thesis was to demonstrate a safe, innovative method of directly quantifying NOx concentrations in a full-scale surface mining environment. A small unmanned aircraft system was used with a continuous gas monitor to sample concentrated fumes. Three flights were completed – two in the Powder River Basin. Results from a moderate NOx emission showed peak NO and NO2 concentrations of 257 ppm and 67.2 ppm, respectively. The estimated NO2 presence following a severe NOx emission was 137.3 ppm. Dispersion of the gases occurred over short distances, and novel geometric models were developed to describe emission characteristics. Overall, the direct sampling method was successful, and the data collected are new to the body of scientific knowledge.

The ability to quantify soil moisture spatial variability and its temporal dynamics over entire fields through direct soil observations using remote sensing will improve early detection of water stress before crop physiological or economic damage has occurred, and it will contribute to the identification of zones within a field in which soil water is depleted faster than in other zones of a field. The overarching objective of this research is to develop tools and methods for remotely estimating soil moisture variability in agricultural crop production. Index-based and machine learning methods were deployed for processing hyperspectral data collected from moisture-controlled samples. In the first of five studies described in this dissertation, the feasibility of using “low-cost” index-based multispectral reflectance sensing for remotely delineating soil moisture content from direct soil and crop residue measurements using down-sampled spectral data were determined. The relative reflectance from soil and wheat stalk residue were measured using visible and near-infrared spectrometers. The optimal pair of wavelengths was chosen using a script to create an index for estimating soil and wheat stalk residue moisture levels. Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples; however, this index worked poorly on estimating stalk residue moisture. In the second of five studies, 20 machine learning algorithms were applied to full spectral datasets for moisture prediction and comparing them to the index-based method from the previous objective. Cubic support vector machine (SVM) and ensemble bagged trees methods produced the highest composite prediction accuracies of 96% and 93% for silt-loam soil samples, and 86% and 93% for wheat stalk residue samples, respectively. Prediction accuracy using the index-based method was 86% for silt-loam soil and 30% for wheat stalk residue. In the third study, a spectral measurement platform capable of being deployed on a UAS was developed for future use in quantifying and delineating moisture zones within agricultural landscapes. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous reflectance data acquisition. A similar ground-based system was developed to keep track of ambient light during reflectance target measurement. The systems were tested under varying ambient light conditions during the 2017 Great American Eclipse. In the fourth study, the data acquisition system from the third study was deployed for recognizing different targets in the grayscale range using machine learning methods and under ambient light conditions. In this study, a dynamic method was applied to update integration time on spectrometers to optimize sensitivity of the instruments. It was found that by adjusting the integration time on each spectrometer such that a maximum intensity across all wavelengths was reached, the targets could be recognized simply based on the reflectance measurements with no need of a separate ambient light measurement. Finally, in the fifth study, the same data acquisition system and variable integration time method were used for estimating soil moisture under ambient light condition. Among 22 machine learning algorithms, linear and quadratic discriminant analysis achieved the maximum prediction accuracy. A UAS-deployable hyperspectral data acquisition system containing three portable spectrometers and an embedded computer was developed to classify moisture content from spectral data. Partial least squares regression and machine learning algorithms were shown to be effective to generate predictive models for classifying soil moisture.

Unmanned Aircraft Systems (UAS) represent a significant potential for growth in the aerospace industry. Their use in military operations has increased exponentially in the last decade alone, requiring a corresponding increase in training airspace in the United States. In addition to military usage, UAS have the potential to fulfill a myriad of roles for both the public and private sectors. However, the use of UAS has been limited in the National Airspace System (NAS) to military and public applications and only under fairly restrictive Certificates of Authorization or Waiver (COA). The only way to truly realize the potential of UAS is to fully integrate them into the NAS. The desire to integrate UAS was recently codified into law with the 2012 FAA Modernization Act, mandating integration by specific, fairly short timelines. There are several challenges currently preventing the full integration of UAS that range from technological to procedural areas. However, the one common theme in all of these challenges is Safety. Across the literature on this topic there is no consensus on how safe UAS need to be to achieve integration, whether UAS can currently meet specified safety targets, and if not, what is the best way to achieve the safety goals. The purpose of this effort was to demonstrate a comprehensive framework for analyzing UAS integration efforts using a Target Level of Safety (TLS) approach. Using reliability tools, aircraft encounter models, and data from a wide variety of sources ranging from manned aircraft safety, explosives, falling debris and earthquake damage, the primary outcome of the effort was a better understanding of the risk to second and third party persons as a result of UAS operations in the NAS. This framework and associated models are validated using reliability and casualty data from manned aircraft operations. The framework is then applied to several relevant and specific cases to demonstrate the impact of policy decisions on UAS reliability and allowed operational areas. The supporting research and analysis can serve as a baseline for future integration analysis and decision-making efforts, and was designed to allow stakeholders and decision makers in this field to assess UAS safety, and set minimum system reliability requirements and mitigation system effectiveness standards.

Due to the complexities of modern military operations and the technologies employed on today's military systems, acquisition costs and development times are becoming increasingly large. Meanwhile, the transformation of the global security environment is driving the U.S. military's own transformation. In order to meet the required capabilities of the next generation without buying prohibitively costly new systems, it is necessary for the military to evolve across the spectrum of doctrine, organization, training, materiel, leadership and education, personnel, and facilities (DOTMLPF). However, the methods for analyzing DOTMLPF approaches within the early acquisition phase of a capability-based assessment (CBA) are not as well established as the traditional technology design techniques. This makes it difficult for decision makers to decide if investments should be made in materiel or non-materiel solutions. This research develops an agent-based constructive simulation to quantitatively assess doctrine alongside materiel approaches. Additionally, life-cycle cost techniques are provided to enable a cost-effectiveness trade. These techniques are wrapped together in a decision-making environment that brings crucial information forward so informed and appropriate acquisition choices can be made. The methodology is tested on a future unmanned aerial vehicle design problem. Through the implementation of this quantitative methodology on the proof-of-concept study, it is shown that doctrinal changes including fleet composition, asset allocation, and patrol pattern were capable of dramatic improvements in system effectiveness at a much lower cost than the incorporation of candidate technologies. Additionally, this methodology was able to quantify the precise nature of strong doctrine-doctrine and doctrine-technology interactions which have been observed only qualitatively throughout military history. This dissertation outlines the methodology and demonstrates how potential approaches to capability-gaps can be identified with respect to effectiveness, cost, and time. When implemented, this methodology offers the opportunity to achieve system capabilities in a new way, improve the design of acquisition programs, and field the right combination of ways and means to address future challenges to national security.

The operation of sophisticated unmanned aircraft systems (UAS) involves complex interactions between human and machine. Unlike other areas of aviation where technological advancement has flourished to accommodate the modernization of the National Airspace System (NAS), the scientific paradigm of UAS and UAS user interface design has received little research attention and minimal effort has been made to aggregate accurate data to assess the effectiveness of current UAS human-machine interface (HMI) representations for command and control. UAS HMI usability is a primary human factors concern as the Federal Aviation Administration (FAA) moves forward with the full-scale integration of UAS in the NAS by 2025. This study examined system learnability of an industry standard UAS HMI as minimal usability data exists to support the state-of-the art for new and innovative command and control user interface designs. This study collected data as it pertained to the three classes of objective usability measures as prescribed by the ISO 9241-11. The three classes included: (1) effectiveness, (2) efficiency, and (3) satisfaction. Data collected for the dependent variables incorporated methods of video and audio recordings, a time stamped simulator data log, and the SUS survey instrument on forty-five participants with none to varying levels of conventional flight experience (i.e., private pilot and commercial pilot). The results of the study suggested that those individuals with a high level of conventional flight experience (i.e., commercial pilot certificate) performed most effectively when compared to participants with low pilot or no pilot experience. The one-way analysis of variance (ANOVA) computations for completion rates revealed statistical significance for trial three between subjects [F (2, 42) = 3.98, p = 0.02]. Post hoc t-test using a Bonferroni correction revealed statistical significance in completion rates [t (28) = -2.92, p<0.01] between the low pilot experience group (M = 40%, SD =. 50) and high experience group (M = 86%, SD = .39). An evaluation of error rates in parallel with the completion rates for trial three also indicated that the high pilot experience group committed less errors (M = 2.44, SD = 3.9) during their third iteration when compared to the low pilot experience group (M = 9.53, SD = 12.63) for the same trial iteration. Overall, the high pilot experience group (M = 86%, SD = .39) performed better than both the no pilot experience group (M = 66%, SD = .48) and low pilot experience group (M = 40%, SD =.50) with regard to task success and the number of errors committed. Data collected using the SUS measured an overall composite SUS score (M = 67.3, SD = 21.0) for the representative HMI. The subscale scores for usability and learnability were 69.0 and 60.8, respectively. This study addressed a critical need for future research in the domain of UAS user interface designs and operator requirements as the industry is experiencing revolutionary growth at a very rapid rate. The deficiency in legislation to guide the scientific paradigm of UAS has generated significant discord within the industry leaving many facets associated with the teleportation of these systems in dire need of research attention. Recommendations for future work included a need to: (1) establish comprehensive guidelines and standards for airworthiness certification for the design and development of UAS and UAS HMI for command and control, (2) establish comprehensive guidelines to classify the complexity associated with UAS systems design, (3) investigate mechanisms to develop comprehensive guidelines and regulations to guide UAS operator training, (4) develop methods to optimize UAS interface design through automation integration and adaptive display technologies, and (5) adopt methods and metrics to evaluate human-machine interface related to UAS applications for system usability and system learnability.

Unmanned aircrafts represent one of the fastest growing technologies of the last years. Although historically not completely unknown, only recently have they acquired the long-deserved attention. However, it focuses almost exclusively on their use in combat operations, whereas their capabilities in civilian sphere remain relatively unexplored. Analysis of these abilities is the main focus of this thesis, which describes and analyses current applicable legislation, points out different issues of both its interpretation and application and tries to offer solutions. This thesis is divided into 4 chapters and a conclusion. The first chapter deals with the introduction to this topic, including history of unmanned aircrafts and clarification of the term unmanned aircraft itself. Then it moves on to describe the possible classification of unmanned aircrafts and the terminology used, which remains unsettled even to this day. The main aim of this is to try to help the reader to find his way in this complicated area. The second chapter includes the analysis of the applicable legislation in the Czech republic, starting with the general approach and then continuing with special emphasis on particular issues, such as operating limitations of unmanned aircrafts in certain areas or liability of aircraft operators...

The aim of this work is to propose a construction solution for remote controlled flying machine and to propose way of its management. The work deals with the general way to construct a flying machine with vertical start and concrete solution for construction.

<p>Due to their rapid technological advancement, mobile sensors such as unmanned aerial vehicles (UAVs) are seeing growing application in the area of multi-target tracking and identity management (MTIM). For efficient and sustainable performance of a MTIM system with mobile sensors, proper algorithms are needed to both effectively estimate the states/identities of targets from sensing data and optimally guide the mobile sensors based on the target estimates. One major challenge in MTIM is that a target may be temporarily lost due to line-of-sight breaks or corrupted sensing data in cluttered environments. It is desired that these targets are kept tracking and identification, especially when they reappear after the temporary loss of detection. Another challenging task in MTIM is to correctly track and identify targets during track coalescence, where multiple targets get close to each other and could be hardly distinguishable. In addition, while the number of targets in the sensors’ surveillance region is usually unknown and time-varying in practice, many existing MTIM algorithms assume their number of targets to be known and constant, thus those algorithms could not be directly applied to real scenarios.</p> <p>In this research, a set of solutions is developed to address three particular issues in MTIM that involves the above challenges: 1) using a single mobile sensor with a limited sensing range to track multiple targets, where the targets may occasionally lose detection; 2) using a network of mobile sensors to actively seek and identify targets to improve the accuracy of multi-target identity management; and 3) tracking and managing the identities of an unknown and time-varying number of targets in clutter.</p>

Safety is the top priority for the aviation industry and a safe airport environment is essential to aviation safety. However, due to the increasing prevalence of UAS in recent years, UAS sightings have become a potential threat to airports. When UAS appear in the vicinity of airports, they bring safety concerns and result in negative operational and economic impacts on airports. Since the FAA’s mission is to provide the safest and most efficient aerospace system in the world, further research regarding the threat of UAS sightings to airports is needed. The purpose of this study is to investigate the threat of UAS to airports and in the national airspace system (NAS). This study includes three primary components: the analysis of 6,551 Federal Aviation Administration (FAA) UAS sighting reports, a case study of the impacts of the UAS sighting at Newark Liberty International Airport (EWR) on January 22, 2019, and a synthesis of airport operator perspectives based on interviews with airport personnel at five airports. The analysis of UAS sighting reports shows the characteristics of UAS sightings, the case study on EWR UAS illustrates the impact of the UAS sighting at the airport, and interview results illustrate the current perspective of airport operators regarding the risk of UAS. Along with the results, the scientific methods of identifying and analyzing the characteristics of UAS sightings in controlled airspace close to airports could be used by researchers to study UAS sightings in the future. Findings from this study may be beneficial to multiple stakeholders, including airport personnel, regulators, entrepreneurs, and vendors in the aviation industry. <br>