Drone Compliance Standards

Automated system for authorizing and coordinating unmanned aerial vehicle (UAV) flights to simplify regulatory compliance and mitigate airspace conflicts. The system involves a cloud-based platform that receives UAV flight requests with proposed geofences, approves or denies based on existing allocations, and sends approved geofences to UAVs. UAVs then limit movements to the approved geofence. The system can also suggest alternate paths if estimated fuel levels indicate insufficient for the planned route. This automates UAV airspace coordination and compliance.

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Automated planning and approval system for unmanned aerial vehicles (UAVs) to enable safe and efficient flight operations in regulated airspace. The system takes a UAV mission specification, generates candidate flight trajectories based on UAV capabilities, restrictions, and airspace rules, and selects an optimized trajectory. This is done to provide approved flight plans for UAVs that avoid conflicts with other aircraft. The system models maneuvers from a library, validates trajectories, and deconflicts them with other flights. It aims to provide automated UAV flight planning and approval for regulated airspace, enabling safe integration of UAVs into the national airspace system.

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A software development kit (SDK) for managing access of unmanned aerial vehicles (UAVs) to restricted areas using geo-fencing. The SDK allows applications on UAVs to request access data based on their location from an access manager. The access data determines if the UAV can enter certain regions. The access manager provides the access data to the UAV via its connection. This enables dynamic, fine-grained location access control for UAVs instead of fixed restricted zones.

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Detecting unauthorized drones that connect to cellular networks. The method involves a network element like an access and mobility management function (AMF) sending a tracking indication message to cell towers when drone attributes meet certain criteria. The towers then report back the drone's attributes. By analyzing these reported attributes, the AMF can determine if the drone is authorized or not. This allows identifying unauthorized drones that have connected to cellular networks.

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Method for controlling communication between unmanned aerial vehicles (UAVs) and their controllers using a wireless network to enable authorized UAV-UAV and UAV-controller communication while preventing unauthorized flights. The method involves registering UAVs and controllers for UAS services with a central traffic management system. When a UAV requests a network session, the network checks if the destination UAV/controller is allowed through the management system. If so, it sets up the session. This ensures UAVs can only communicate with authorized targets.

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User interface for creating and modifying airspace restrictions for drones using non-digitized information. The interface allows human operators to quickly and easily convert analog information like phone calls and radio transmissions into digital airspace restrictions for drones. It provides fields for selecting restriction types, durations, locations, descriptions, and a task list to manage and access the restrictions. This enables efficient ingestion of non-digitized information into the drone fleet management system for creating or modifying airspace restrictions.

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Introduction. With the growing popularity of unmanned aircraft, many challenges anWith possibilities aviation use, there are and threats to state security. Effective introduction new technologies should be accompanied by creation an appropriate legal framework unified terminological apparatus. Currently, is a problem diversity in area question, affecting efficiency regulation. Methods. The research was based on general scientific methods such as analysis, categorisation, classification, typology statistical method, well private formal-legal, comparative-legal historical-legal methods. Results. As result research, author provides classification aerial vehicles (hereinafter referred UAVs) using characteristics aircraft systems UASs). These results can applied law enforcement practice qualification offences related use UASs, development normative acts effective measures counteract coming from unlawful UAVs. theoretical significance article consists methodologically verified functionally substantiated UAVs, eliminating uncertainty this area.

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Detect and Avoid (DAA) mechanisms for unmanned aerial systems (UAS) to enable safe beyond visual line of sight (BVLOS) operation. The DAA mechanism involves direct communication between UAS and network assistance for collision detection and conflict resolution. UAS register their DAA capabilities with a server. When requested by a serving airspace management unit (AMU), the UAS configures its DAA policy. If collision is detected, the UAS dynamically changes its flight path based on the policy. This enables network-assisted DAA beyond onboard sensors' range.

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ABSTRACT The European Union (EU) is making regulatory efforts to allow for the safe integration of drones into civilian airspace through automated means. Commission Implementing Regulation 2021/664 concerning unmanned traffic management (a system referred as USpace) furthers that commitment. Accordingly, drone operators must avail themselves automatic trafficrelated USpace services flight authorization, geo awareness, information, and network identification before entering USpace airspace. Using infrastructural analysis, proposed within Science Technology Studies (STS), this article shows while automation meant ensure traffic, it could also challenge safety in at least three ways: by inviting reliability concerns infrastructure; because transition from humancentric automationcentric systems; being subject irregularities EU's framework. An therefore, helps unveiling important factors which influence technologies critically discussing role law their regulation.

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A system for securely managing and verifying the identities of unmanned aerial systems (UAS) to enable safe integration of drones into airspace. The system provides a dynamic secure identification network that allows aircraft to share identification information through a permissioned blockchain-like network. It provides services like identity provisioning and verification to securely ingest private registration data, UAS identity verification requests, and returns validated identity and flight information. The system enables a standards-compliant utility service that functions with the Remote ID standard.

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Monitoring unmanned aerial systems (UAS) in a communication network to enable law enforcement to track and intercept UAS communications and locate UAS devices. It involves a network function requesting info about UAS devices from the mobility management, subscription management, and payload management devices. This provides data like UAS device IDs, subscriber info, flying rules, location, and intercepted comms. A central function collects and sends this to law enforcement.

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Enabling safe unmanned aerial vehicle (UAV) operations beyond visual line of sight by using ground-based and UAV-mounted observer devices to provide visual monitoring of the airspace and UAVs. The observer devices can be deployed around a designated area to cover it and provide a visual line of sight. UAVs with observer devices can fly to positions, land, hover, or autonomously to monitor other UAVs and conditions. This replaces human visual observers when not practical for BVLOS, high altitude, or unattended UAV flights.

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Enabling unmanned aerial vehicles (UAVs) to integrate safely into civilian airspace by leveraging wireless networks for detect and avoid (DAA) functionality. UAVs discover network-based DAA services, register to participate, and then use the network infrastructure for supplemental collision avoidance assistance beyond onboard sensors. This involves the UAV indicating association, the network confirming DAA availability, and the UAV registering to leverage network resources like localized DAA servers.

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Optimizing power control for unmanned aerial vehicles (UAVs) using orientation-based open loop power control (OLPC) for SL transmission of remote ID. The OLPC takes into account the UAV's orientation relative to a reference location to determine the optimal transmission power. This allows balancing between ensuring ground receipt and avoiding interference while UAVs fly at different heights and angles. The reference location could be a nearest base station or a predefined location.

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Ensuring safe operation of industrial machines like drones or agricultural equipment before starting the operation by checking if the operation area is registered. The system involves an industrial machine, an area acquisition device, and a control unit. The control unit has storage to store registered areas. When the machine requests to operate in a new area, the control unit checks if that area matches any registered areas. If not, it prevents the operation to avoid issues like drones spreading chemicals outside registered fields or collisions due to unidentified obstacles. This provides a safeguard against misuse and errors by confirming the operation area against known data.

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An acoustic-based detection and avoidance system for unmanned aircraft that uses a array of audio sensors to detect and locate nearby aircraft without radar. The system analyzes audio signals received by the sensors to estimate the position of intruding aircraft relative to the UAV. By detecting audio signals from other aircraft and analyzing their characteristics like frequency and timing, the system can determine the direction, range, and altitude of nearby aircraft without relying on heavy radar equipment. This allows UAVs to meet regulatory requirements for 360-degree detection and avoidance in a more cost-effective and lightweight manner compared to radar.

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Processing method to detect and prevent abnormal unmanned aerial vehicle (UAV) behavior like transmitting non-command and control (non-C2) data. The method involves a session management function (SMF) network element providing detection rules to user plane function (UPF) network elements. UPFs then detect non-C2 data in UAV transmissions and report back to SMF. SMF sends abnormal traffic reports to a network element with supervision capabilities to process and prevent transmission of non-C2 data. This allows detecting and stopping abnormal UAV behavior without disconnecting or switching controllers.

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Introducing drones in the Indian airspace is helping to improve tasks like security, movement of goods, rescue operations, and agricultural monitoring. This research looks into what happening now, UAVs are being used for, regulatory guidelines, limitations faced when using aviation sector India. Through use secondary data sources, policy documents, comments from experts, paper details how recent release Drone Rules 2021 has driven innovation at same time exposed shortcomings infrastructure, privacy protections, skilled employees. It clear study that although drone India developing fast, it will need support everyone become both sustainable scalable. text aimed outlining ways India, as well giving ideas for ensuring better management safety airspace. Keywords- drones, UAVs, aviation, DGCA, regulations, logistics, surveillance, integration

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The economic and social implications of the expanding drone ecosystem push need to extend Air Traffic Management implement Unmanned aircraft system Management. U-space, an European initiative launched in 2016, aims design a for managing integrating drones airspace, as well set services procedures guarantee airspace access large number drones. However, entry new users operations brings concerns, increasing risks cyberattacks are major factor U-space concerns. Indeed, standalone inherently introduce cyber threats that similar ones carried over by autonomous vehicles. Furthermore, involves many highly-exposed Cyber-Physical Systems, with growing vulnerabilities potentially conducting several threat scenarios security incidents affecting public spaces, critical facilities, citizens right privacy. These considerations lead necessity establish primary challenge proper security-by-design approaches. From research perspective, these result adoption automated risk assessment addressing complexity cost-efficiency engineering secure systems. Following this direction, work proposes preliminar... Read More

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Aerial traffic management system for unmanned aerial vehicles (UAVs) that enables efficient navigation through complex airspace. The system employs a centralized control server to generate navigation plans for multiple UAVs, with each plan specifying waypoints. When a UAV encounters an unoccupied waypoint, it is instructed to hover until clearance is obtained. Once cleared, the UAV proceeds to its destination. The system ensures safe separation between UAVs by dynamically locking occupied waypoints, enabling coordinated navigation through shared airspace.

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Automated flight response for unmanned aerial vehicles (UAVs) to comply with airspace restrictions around airports and other no-fly zones. The UAV calculates its distance from restricted areas using its own location and the location of the restrictions. If the distance is below a threshold, it takes immediate action like landing or preventing takeoff. If the distance is greater, it allows normal flight. This allows automated compliance with airspace rules while providing flexibility for safe operations when farther away.

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The aim of this article is to discuss the legal changes introduced in Poland response growing threat critical infrastructure facilities from unmanned aerial vehicles (UAVs) and present a recommended method for assessing effectiveness UAV detection neutralisation systems developed by Government Centre Security (RCB). authors amendment Act 3 July 2002 Aviation Law, which includes provisions relevant safety platform flights, ensuring that operators have right defend protected facility against attacks using aircraft, 24 May 2013 on direct coercive measures firearms, expanded catalogue include destruction or immobilisation seizure control over its flight, indicated means neutralisation.

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Detecting drones approaching restricted areas and preventing them from entering using 5G network technology. When drones are detected near restricted areas like airports, military bases, or government buildings, the network reduces the power of beams that could steer the drones into those areas. This is done by having the base stations near the restricted area disable certain beams and reduce the power of others. This forces the drones to use alternative beams that avoid the restricted area. This creates a virtual geofence around the restricted area using 5G network capabilities. The network also enables beamforming on the base stations near restricted areas to steer drones away. This involves detecting drones approaching, estimating which beams they will use, and disabling or powering down those beams to force them to use different beams.

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Detecting and responding to flight-restricted regions for unmanned aerial vehicles (UAVs) to permit automated flight control in response to detected proximity to restricted areas. The method involves calculating distances between the UAV and flight-restricted regions using location data. If the distance falls within certain thresholds, flight response measures like landing or preventing takeoff are initiated. This provides automated response to restricted areas with graded actions based on proximity. It also uses location systems to accurately detect restricted areas.

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Automating drone waiver requests in emergency situations to expedite approval and reduce delays. The system uses AI to identify and describe incidents, communicate instantly with requesters, and validate data. It aims to provide faster, more efficient waiver processing for emergency drone operations compared to manual methods.

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When operating an unmanned aircraft system within the specific category beyond Air Risk Class a or certified category, detect and avoid is required in order to mitigate risk of colliding with manned aircraft. To demonstrate compliance regulatory requirements, several standards have been developed that propose range technologies requirements for different operational environments. This paper represents entry point understanding key aspects this application area. Important concepts, like remain well clear, collision avoidance, encounter modeling, are elucidated. Furthermore, available systems presented. The introduced documents analyzed summarized respect their applicability, scope, major characteristics. Further, comparison between given where possible. Some describe corresponding entirety, while others focus on equipment component requirements. Nevertheless, presented sufficient cover wide applications. Essential these listed condensed manner, enabling reader choose suitable standard operation.

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Managing flights of mobile terminals like drones that use multiple cellular networks. A central management apparatus allocates flight zones across cellular networks based on shared 3D space. This involves dividing airspace into zones identified by latitude, longitude, and altitude. The management apparatus distributes this zone allocation data to the cellular networks. Each network then uses it to coordinate flights of devices connected to their network. This ensures consistent flight management across networks when multiple devices fly in proximity.

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The rise of civilian drone technology is reshaping smart city development, offering new capabilities for urban management, service delivery, and surveillance. However, effective integration drones into the environment requires carefully crafted public policiesespecially in regions like Gulf Cooperation Council (GCC) countries Egypt, where security privacy concerns are paramount. This paper presents a policy framework **network-integrated** cities, emphasizing strategies that facilitate innovation while maintaining safety national security. informed by an analysis current regulations initiatives GCC states as well global best practices unmanned aerial systems (UAS) governance. Key components include regulatory reform to permit controlled use, technological infrastructure airspace management via telecommunications networks, safeguards cybersecurity. results highlight how cities can leverage services under unified guidelines, Egypt might modernize its restrictive stance reap benefits. We discuss implications stakeholdersranging from planners civil aviation authoritiesand offer ... Read More

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Authenticating unmanned aerial vehicles (UAVs) and authorizing UAV services in a wireless communication system. The authentication involves verifying the identity of a UAV terminal and requesting registration from the access management function (AMF). If the UAV is a part of a fleet, the AMF requests UAS authentication from the UAS traffic management (UTM). The UAV also performs mutual authentication with another UAV. This allows controlled pairing and sharing of UAV services between authorized devices.

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Secure and efficient tracking of unmanned aerial vehicles (UAVs) in a geographic area using a network of ground-based transceivers that coordinate with the UAVs to avoid congestion and spoofing. The ground transceivers send interrogation signals to UAVs with instructions on how to respond. UAVs transmit location data back using the specified format. This allows the ground system to authenticate and accurately track UAV positions without periodic broadcasts that can be spoofed.

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Flight management system for unmanned aerial vehicles (UAVs) that fly according to predefined flight plans. The system involves sharing flight plan and real-time UAV position information between the UAV and a management device. This allows accurate tracking of UAV flight status by updating the shared info as needed from the UAV transmissions. It enables safer and more controlled UAV flight by managing UAVs based on the flight plan route rather than just position. It also allows UAVs to autonomously adapt flight if conditions change or if other UAVs are nearby.

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Querying a database of geofences to efficiently enforce rules within them using drones and other devices. The geofences are defined by a plurality of geographic locations associated with unique IP addresses. Drones and devices can request geofence information for a location using the IP addresses. This enables accurate and fast geofence lookup and enforcement by drones without needing GPS or other positioning systems. The geofences can also have rules, licenses, and entitlements associated with them.

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Monitoring drone flights to detect unauthorized flights (aka "black flights") that have not been registered or approved. The monitoring involves comparing the known flight plans of registered drones against real-time drone locations. If a drone is detected flying outside its approved plan, it is flagged as a black flight. The monitoring is performed by supervision base stations that provide slicing services to drones. If a drone is flying black, the base station does not provide slicing to prevent it from using its services. This forces the drone to land or return to a legal flight path.

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Monitoring method for unmanned aerial vehicle (UAV) missions to improve success rate by predicting and mitigating risks during flight. The method involves determining factors and thresholds affecting UAV tasks, like environmental, equipment, and personnel factors. It collects real-time data on these factors during flight and uses prediction models to forecast future values. If forecasts exceed thresholds, it generates early warnings with improvement suggestions. If thresholds are not exceeded, it uses an evaluation algorithm to calculate a monitoring report based on the forecasts, weights, and operator quality. This provides proactive risk assessment and recommendations throughout the mission.

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Automated drone airport system for reliable and safe operation of drones in applications like solar panel inspection, reservoir monitoring, etc. The system includes separate components like a master control server, ground station, network communication, power supply, and battery management. It connects to an external server for task assignment. The server checks environmental suitability before releasing the drone. The ground station controls the drone remotely. The components like hatches, platforms, and positioning devices ensure safe drone entry/exit, landing, and recovery. The system enables automated drone deployment, operation, data transfer, and battery swapping.

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An authorization system for unmanned aerial vehicles (UAVs) that restricts the operation of UAVs and equipment based on the user's license. The system involves an off-board controller that checks the license, determines authorized equipment/software profiles, and selectively transmits that software to the UAV for installation. This prevents unauthorized equipment operation even if assembled on the UAV.

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Manage and restrict drone operations and equipment to prevent unauthorized use and avoid bad actors gaining access to restricted capabilities. It involves an off-board computer that checks user licenses and selectively provides equipment control software to the drone based on the license. If a license authorizes certain equipment, it will transmit the proper software to operate it. This ensures the drone cannot operate unauthorized equipment.

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Safe operation method for unmanned aerial vehicles (UAVs) that enables real-time monitoring and control of UAV flight safety. The method involves calculating flight risk parameters based on factors like UAV performance, environmental complexity, and task difficulty. This allows determining the current flight strategy to mitigate risks. The performance parameters are derived from UAV sensors and historical data. The environmental complexity is calculated using weather, terrain, etc. The task complexity is based on factors like distance, load, and number of flights. By dynamically assessing risk, the UAV can adapt flight plans to account for changing conditions and avoid unsafe situations.

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A system for tracking and analyzing unmanned aerial vehicle (UAV) flights to monitor compliance with regulations and identify illegitimate use. The system collects aircraft and flight data through web crawling and analysis to determine UAV flight purposes. It uses a radio frequency identification (RFID) module to compare flight patterns and classify flights as legitimate or illegitimate. If an unregistered UAV is detected, the system contacts authorities to monitor and track it.

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Risk management system for unmanned aerial vehicle (UAV) inspection operations to improve safety and efficiency. The system has three modules: airspace approval, flight planning, and comprehensive management. The airspace approval module reviews UAV inspection plans to assess risks. Flight planning generates detailed UAV routes based on inspection plans. Comprehensive management monitors real-time UAV flights against planned routes to detect deviations and abnormalities.

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Method, device, and system for supervising unmanned aerial vehicles (UAVs) that allows independent and reliable monitoring without relying on the UAV operator's cloud platform. The method involves obtaining reporting information from UAV operators that includes UAV ID, operator-provided UAV ID, and hardware ID. By cross-referencing the communication ID with the hardware ID provided by the network operator, compliance with regulatory flight requirements can be determined. This allows monitoring platforms to independently verify UAV compliance without relying on operator-reported data.

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Ensuring safe drone flights over public roads and other restricted/sensitive locations by having drones broadcast their flight paths and positions using vehicle-to-everything (V2X) messaging. Nearby vehicles, pedestrians, and other drones can then use this information to warn each other and adjust flight paths to avoid collisions. The drones can also detect proximity to roads based on signals from vehicles, devices, or infrastructure. If they are near roads with people, they can temporarily avoid flying over them.

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Collision avoidance system for multiple drones that alerts operators when drones are in proximity to each other to prevent mid-air collisions. The system has a central collision warning device that receives flight information from each drone's operator. It generates virtual zones around the drones based on their positions. It checks if these zones intersect with each other or with known obstacle zones. If so, it generates an alarm to alert the drone operators of potential collisions. This provides collision avoidance without needing cameras or expensive sensor systems on each drone.

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Monitoring and supervising unmanned aerial vehicles (UAVs) to ensure safe operation. The method involves using the UAV's own communication capabilities to transmit flight data to a central server. This allows real-time monitoring of UAV positions and flight parameters. The server can generate flight restriction instructions and warnings if regulations are violated. UAVs can also obtain local airspace restrictions. Offline UAVs can submit flight plans with parameters for approval.

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A system to identify, monitor, and control unregistered drones that have not gone through a registration process. The system uses communication between the drones and a base station to determine if a drone is registered or not. If unregistered, the base station can alert important facilities, monitor the drone, and prevent accidents. The drones provide real-time flight data like speed and altitude. The registration process involves providing flight details like purpose, equipment, and location to the base station.

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Monitoring method, device and user terminal for supervising and managing unmanned aerial vehicles (UAVs) using blockchain. The method involves receiving job broadcasts from UAVs on the blockchain network, verifying if the flight plans match locally stored ones, and broadcasting adjustment notifications if there are inconsistencies. This allows real-time monitoring and enforcement of UAV flight plans using blockchain as a shared ledger.

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Monitoring system for tracking and analyzing drone flights to identify unregistered drones and determine flight purposes. The system collects aircraft information like manufacturer, performance, and financing through web crawling. It also tracks flight data like location, speed, and altitude. This data is analyzed to classify flight purposes and identify unregistered drones. The system can also communicate with drones to trace them back if lost. By monitoring and classifying drone flights, it can detect unregistered drones and determine purposes for registered ones.

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Automated system for preparing safety cases for drone operations and dynamically generating flight plans to mitigate risks. The system uses data sources like public databases to gather parameters for a proposed drone flight without manual fieldwork. It analyzes the retrieved data to identify hazards and determine risks. A flight plan is then determined based on the risk level. This allows faster, less expensive safety case preparation compared to manual data gathering. The system optimizes the flight plan to minimize risks while meeting user objectives.

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Quantitative risk assessment framework for unmanned aerial vehicles (UAVs) that enables quantification of traditionally entered qualitative data. The framework takes inputs like flight plans, vehicle health, operator experience, etc. and maps them to numerical calculations for ground risk and airborne risk. These calculated scores are then used to reference predetermined categorizations for risk assessment and remediation actions. This provides quantitative, repeatable and predictable risk management for UAVs that addresses limitations of qualitative methods.

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Pilot management terminal and unmanned aircraft monitoring system to ensure safe and legal operation of unmanned aircraft. The system connects unmanned aircraft pilots and their drones using a central flight service platform. The pilot terminal collects pilot data and transmits it to the platform. The platform has an aircraft database, pilot database, and management module. This allows monitoring and management of both pilots and drones through the platform.

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