Search Results (27)

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports. Full article

The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security, operability among UTM stakeholders, and legal compliance. As such, we propose DFly, a publicly auditable and privacy-preserving UAS traffic management system on Blockchain, with two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero-knowledge proofs enables the deployment of an automated, distributed, transparent, and privacy-preserving Flight Authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proposed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees. Full article

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper${}^{\circledR }$, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs. Full article

This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs. Full article

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results. Full article

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, in building a safe path, the UTM needs to determine the time and space allocated to each flight. Ideas for discretizing and structuring airspace in various forms have been proposed to enhance the efficiency of system operation and improve traffic congestion through effectual airspace allocation. Additionally, various methods of allocating UAVs to structured unit spaces have been studied in the literature. In this paper, the methods and structural designs for allocating airspace that have appeared in related studies are classified into several types, and their strengths and weaknesses are analyzed. The structured airspace designs are categorized into three models: Air-Matrix, Air-Network, and Air-Tube, and analyzed according to their sub-structures and temporal allocation methods. In addition, a quantitative analysis is conducted by re-categorizing the structured airspace and operation methods and building their combinations. Full article

In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems. Full article

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used. Full article

With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. This could be achieved by establishing the separation requirements for the UTM traffic based on the flight dynamics and communication navigation surveillance (CNS) performance that could be achieved in the airspace in question. A modified Reich collision risk model, typically used in civil aviation for separation minima evaluation, was used for the evaluation of the initial separation that would meet the target level of safety within a prescribed look-ahead time. This paper presents the parametric evaluation of using this physics-based and Monte Carlo-driven Reich collision risk model to evaluate the separation recommendation needed to achieve ${10}^{-7}$ mid-air-collision risk in UTM. The evaluation was conducted for an encounter pair consisting of identical ∼1.2 kg quadrotors with various encounter geometries, cruise velocities, navigation uncertainties, and communication latency. Full article

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators. Full article

Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario. Full article

In the context of the future urban mobility airspaces, U-space lays the foundation for unmanned aircrafts to be integrated with conventionally manned traffic to offer a multitude of services to citizens and businesses, such as urban public transport, goods movement, parcel delivery, etc. Given these extensive possibilities, it is estimated that thousands of aircrafts (or even more) from different owners will share common airspace volumes, and the probability that all of them will travel without colliding is extremely low if no means are put in place to prevent it. This paper aims to introduce the reader to the conflict management policy proposed in U-space and proposes a new version of the PCAN (Prediction-based Conflict-free Adaptive Navigation) algorithm to adapt to this policy. In particular, the new implementation of PCAN takes into account a more than likely classification of flights according to their priority. The evaluation of this priority-aware algorithm shows that it is still possible to avoid any conflict between the set of aircraft in flight, with an insignificant detriment to higher priority flights, while maintaining minimal detriment on average. Full article

Visibility and communication are the essential pillars for safe flight operations in dense airspaces. Small Unmanned Aerial Vehicles (UAVs) of the order of up to 25 kg are increasingly being used at airports as a cost-effective alternative for maintenance and calibration work. However, the joint operation of manned and unmanned aircraft in busy airspaces poses a major challenge. Due to the small diameter of such UAVs, the established principle of “see and avoid” is difficult or even impossible to implement, especially during take-off and landing. For this reason, a certified Mode A/C/S transponder supporting ADS-B was extended with an embedded system and a cellular interface to realize a Multi-Mode-Transceiver (MMT). Integrated into a UAV, the MMT can provide aircraft visibility in the context of traditional manned Air Traffic Management (ATM) and future UAS Traffic Management (UTM) at the same time. This multimodal communication approach was investigated in flight test campaigns with two commercially available UAS that were connected to an experimental UTM with a simulated controlled airspace. The results confirm the safety gain of the multimodal cooperative approach. Furthermore, the collaborative interface with ATC enables the digital transmission of transponder codes, entry clearances and emergency procedures without the need for a voice radio communication. However, the parallel operation of both radio technologies in a confined space requires modifications to the transmission power and alignment of the radio antennas to avoid mutual interference. Furthermore, different reference planes of barometric altitude measurement in manned and unmanned aviation pose additional challenges that need to be addressed. Full article

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation. Full article