The integration of unmanned aerial systems (UAS) into the already complex global aviation system presents new and unique hazards. While many studies have addressed the potential safety concerns of UAS integration, little research has been dedicated to the potential security implications. This study sought to identify potential uses and adaptations of civil UAS systems as weapons of terrorism or crime and potential UAS defenses. Researchers examined 68 academic studies, unclassified government reports, and news articles using Conceptual Analysis to systematically capture and categorize various threats. Using the collected data, researchers developed a UAS threat model for categorically evaluating potential threats. Evaluating UAS defense methodologies, researchers developed a five-layer, defense-in-depth model for protecting assets and individuals from UAS threats.

One key challenge of integrating Unmanned Aircraft Systems (UAS) platforms into the National Airspace System (NAS) is the potential for midair collisions between manned aircraft and the unmanned system. The lack of an established UAS benchmark for Detect, Sense & Avoid Systems put the preponderance of avoidance efforts on manned aircraft pilots to visually see and avoid potential collision threats. The small size, unusual configurations, and diverse operational applications of unmanned systems make UAS platforms difficult to visually identify. This paper sought to determine the mean visibility distance of small UAS systems (sUAS) to an alerted pilot flying a general aviation aircraft in visual meteorological conditions (VMC). The study evaluated mean visibility distance to various sUAS platforms based on a scripted set of UAS convergence conditions. The study utilized a mixed method design in which a general aviation aircraft was flown into a UAS operations area. Study pilots were instructed to locate a flying UAS aircraft without bearing assistance. Both the UAS and manned aircraft were assigned vertically de-conflicted altitudes with the UAS aircraft executing a series of converging and crossing courses relative to the manned aircraft. The distance at which the pilot visually located the UAS platform was timestamped and electronically recorded via a GPS tracking device. The various conditions were analyzed to determine significant visibility differences among the various convergence conditions. Qualitative data was collected from participant comments and observations recorded by an in-flight safety observer.

This paper presents a practical approach to classifying aviation safety reports in an operational context. The goals of the research are as follows: (a) successfully demonstrate a replicable, practical methodology leveraging Natural Language Processing (NLP) to classify aviation safety report narratives; (b) determine the number of reports (per class) required to train the NLP model to achieve an F₁ performance score greater than 0.90 consistently; and, (c) demonstrate the model could be implemented locally, within the confines of a typical corporate infrastructure (i.e., behind the firewall) to allay information security concerns. The authors purposefully sampled 425 safety reports from 2019 to 2021 from a university flight training program. The authors varied the number of reports used to train an NLP model to classify narrative safety reports into three separate event categories. The NLP model’s performance was evaluated both with and without distractor data, running 30 iterations at each training level. NLP model success was measured using a confusion matrix and calculating Macro Average F₁-Scores. Parametric testing was conducted on macro average F₁ score performance using an ANOVA and post hoc Levene statistic. We determined that 60 training samples were required to consistently achieve a macro average F₁-Score above the established 0.90 performance threshold. In future studies, we intend to expand this line of research to include multi-tiered analysis to support classification within a safety taxonomy, enabling improved root cause analysis.

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society.

As small unmanned aircraft systems (sUAS) continue to proliferate in the National Airspace System (NAS), near midair collisions are becoming more common. In late 2017, the National Transportation Safety Board released a report detailing the first confirmed midair collision between a sUAS and manned aircraft in the United States. In February 2018, a video of a sUAS maneuvering around a passenger jetliner on approach to a Las Vegas airport went viral on YouTube. Just months later, a helicopter instructor pilot reported performing evasive maneuvers to avoid colliding with a sUAS, resulting in a non-fatal crash. From 2014 to 2018 the Federal Aviation Administration (FAA) recorded 6,117 reports of near encounters between manned and unmanned aircraft within the NAS (Government Accountability Office [GAO], 2018). In their report, the GAO (2018) highlighted the need for additional operational data to aid the FAA’s management of safety risks posed by unmanned aircraft. The purpose of this study was to evaluate aviation interference and safety hazards caused by unmanned aircraft at an airport in Class C airspace. Using a passive RF sUAS detection device known as the AeroScope, the authors collected sUAS operations data for 13 days at Daytona Beach International Airport in Florida. While the study was limited to DJI-manufactured sUAS, the results yielded detailed operational information on 190 sUAS flights that had been conducted during the sampling period. The authors identified several operator behaviors including preferred sUAS models, flight days and times, common operating locations, and operational altitudes. Operational data was compared against published FAA UAS Facility Maps (UASFM) to examine potential risk areas. Additionally, sUAS detections were compared against historical ADS-B information to examine for potential midair collisions, yielding several notable case studies. The authors evaluated the effectiveness of existing geofencing infrastructure and provided recommendations for integration with the Low Altitude Authorization and Notification Capability (LAANC) system. The paper culminates with a proposal for integrating LAANC usage data into existing aviation information sharing infrastructure to improve manned pilot situational awareness of sUAS activity within the NAS.

When operating under Visual Flight Rules, pilots primarily rely on visual scanning to avoid other aircraft and airborne collision threats. Records from the Federal Aviation Administration indicate that near encounters with unmanned aircraft are on the rise, reaching 1,761 reported unmanned aircraft system (UAS) sightings or near-misses in 2016. This study sought to assess the effectiveness of pilot visual detection of UAS platforms that were equipped with strobe lighting. A sample of 10 pilots flew a general aviation aircraft on a scripted series of five intercepts with a small UAS (sUAS) that was equipped with strobe lighting. Participants were asked to indicate when they visually detected the unmanned aircraft. Geolocation information for both the aircraft and sUAS platform was compared to assess visibility distance. Findings were used to evaluate the efficacy of daytime strobe lighting as a method to enhance pilot sUAS detection, visibility, and collision avoidance. Participants detected the unmanned aircraft during 7.7% of the intercepts. Due to a lack of data points, the authors were unable to conclusively determine if strobe lighting improved UAS visual detection. The authors recommend further research to explore the effectiveness of using sUAS-mounted strobe lights for nighttime visual detection.