선박자동식별장치를 이용한 ADS-B 개념 기반의 항공감시용 송수신기의 구현

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

The number of Small Unmanned Aircraft Systems (SUAS) has greatly increased in recent years. And it is expected that this number will continue increasing in the future. The SUAS may be operated in proximity of aerial vehicles (including aircraft) and it may be necessary to detect them electronically. One possibility is to equip SUAS with Automatic Dependent Surveillance – Broadcast (ADS-B) surveillance system operating on 1090 MHz frequency. The purpose of this study is to investigate the impact of SUAS ADS-B 1090 transmissions on the reception of an aircraft ADS-B Extended Squitter by an ADS-B ground station. The systems being considered operate at low altitudes, typically under 500 ft above ground level (AGL). We found that high number of SUAS equipped with ADS-B flying close to ground ADS-B sensors can have significant impact on the operational range up to 40%.

ADS-B (Automatic Dependent Surveillance – Broadcast) involves broadcasting the latitude, longitude, altitude, velocity, identification and other information from on-board systems. Any suitable receiver may receive and process these broadcasted messages, which can be an ADS-B Ground Station which processes the ADS-B messages and generates aircraft reports to be used by Air Traffic Controllers.

In this paper, we research the feasibility of using high rate Automatic Dependent Surveillance – Broadcast (ADS-B) aircraft altitude and velocity information to detect the presence of mountain waves and Mountain Wave Turbulence (MWT) in the vicinity of steep terrain as well as atmospheric waves and turbulence from other sources that are of interest to aviation, for instance, Convective Induced Turbulence (CIT). The key element of ADS-B that enables the research is a 1 second update rate on ADS-B position reports, and aircraft position and altitude being reported based on Global Positioning System (GPS) accuracy. This frequency is much faster than today’s standard of reporting meteorological data via the Aircraft Meteorological Data Relay (AMDAR) or Meteorological Data Collection and Reporting System (MDCRS), and as we show, is fast enough to estimate the location of mountain wave events, MWT, and CIT. When combined with other weather state information gained by in situ sensors, satellite, and radar-based technology in the National Airspace System (NAS), a total situational awareness of mountain wave, MWT, and CIT information in the Continental United States (CONUS) can be achieved for supporting airline flight planning and Air Traffic Management (ATM) decision making.

The University of North Dakota is developing airspace within the state where Unmanned Aircraft Systems (UASs) can be flown without an onboard sense and avoid system or Temporary Flight Restrictions (TFRs). With funding from the U.S. Air Force, a mobile ground-based radar system capable of detecting aircraft operating in Class E airspace and the software to display such information to UAS operators is being developed. The current system uses an Automatic Dependent Surveillance - Broadcast (ADS-B) transceiver to detect any ADS-B-equipped aircraft within the vicinity, and a Ground Control Station (GCS) to detect and control the UAS. Once one or more ground-based radars are integrated into the system, it will also be capable of detecting non-cooperative aircraft (i.e. aircraft that aren't equipped with ADS-B transceivers) operating within the vicinity. The current system uses a portable, high-availability architecture. Since the system is intended to detect potential airspace conflicts from the ground, greater computational power is available to it than to onboard sense and avoid systems. The probability of a midair collision is dependent on the proximity of aircraft to each other, the performance characteristics of the aircraft, and the probabilities of pilots performing basic maneuvers with the aircraft. In this paper the authors present the results of data mining an ADS-B data set from 11 days in early 2010. Probabilistic models of pilot behavior were automatically extracted from the data using a genetic algorithm for cluster analysis.

Application layer interoperability between the surveillance broadcast services system (SBSS) and automatic dependent surveillance - broadcast (ADS-B) avionics is a critical requirement for the next generation air transportation system (NextGen). Demonstrating compatibility between the SBSS and avionics requires the integration of diverse avionics tools, simulated environments tools, RF transmitter test sets, signal analyzers, SBSS equipment, and other receiving systems. ITT has developed a testbed that enables not only validation of the SBSS requirements and RTCA standards, but also provides a window into the user's perspective for various applications. The RF transmitter test set is particularly valuable for generating simulated aircraft flight patterns because it can produce 1090, UAT, and 1030 messages with dynamically programmable content. These ADS-B, Mode S, and ATCRBS messages stimulate SBSS behavior, resulting in transmissions that affect the information and system states held by avionics and air traffic control. A series of simulations with the SBSS and avionics in the loop are conducted and subsequently analyzed.

Flight cancellations, departure delays, congestion in taxi times and airborne holding delays are increasingly frequent problems that negatively impact the performance, fuel burn, emissions rate and customer satisfaction at major airports in the world. However, this is just a brushstroke of the future to come. The dramatic growth in the air traffic levels has become a problem of paramount importance, leading into an increased interest for enhancing the current Air Traffic Management (ATM) systems. The main objective is to being able to cope with the sustained air traffic growth under safe, economic, efficient and environmental friendly working conditions. The ADS-B (Automatic Dependent Surveillance — Broadcast) technology plays a major role in the new ATM systems, since it provides more accurate real-time positioning information than secondary radars, in spite of using a cheaper infrastructure. However, the main flaw in the use of ADS-B technology is the generation of large volumes of data, that, when merged with other flight-related information, faces important scalability issues. In this work, we start off from a previously developed data lake for the support of the full ADS-B data life-cycle in a scalable and cost-effective way, and propose a data architecture to integrate data from different providers and reconstruct flight trajectories that can ultimately be used to improve the efficiency in flight operations. This data architecture is also evaluated using a 2-week testbed which reports some interesting figures about its effectiveness.

The increasing demand for the use of unmanned aerial systems (UAS) in various social and economic applications have pressed aviation authorities to draw up rules and regulations that permit the release of such aircraft in non-segregated airspace. However, issues related to the safety of air traffic operations arise when considering the possibility of coexistence of manned and unmanned aircraft simultaneously. Thus, surveillance plays a key role in monitoring and controlling air traffic in new scenarios. The positional information provided by the Automatic Dependent Surveillance - Broadcast (ADS-B), originally designed to improve situational awareness for pilots and support controllers in air traffic management, interacts with the Sense and Avoid Systems (S&AS) of the UAS in order to avoid exposure to events of loss of separation (AIRPROX) and collisions. This paper presents a qualitative approach to assess safety when using ADS-B systems integrated with UASs in aeronautical operations. In addition, the possibility of using a methodology previously applied on manned systems for assessing safety on UASs is discussed. A new testing platform (PIpE-SEC) is presented as a possible solution for this safety evaluation.

The global positioning system (GPS) is the core element of the next-generation communication, navigation, surveillance, and air traffic management (CNS/ATM) system. However, GPS signals are vulnerable to radio-frequency interference (RFI), which can significantly impact the normal operation of the CNS/ATM system. In order to maintain CNS/ATM system operation during GPS outages and to meet safety requirements, an alternative positioning, navigation and timing (APNT) based on a radio link of the automatic dependent surveillance-broadcast (ADS-B) can be used. This study first evaluates two navigation algorithms for an ADS-B radio link, and the real aircraft ADS-B data collected at the civil aviation authorized ground stations are used to evaluate the positioning performance of the proposed backup aviation navigation and surveillance system. In addition, the main factor affecting the positioning performance of the radio navigation system is the geometric distribution of ground stations. Therefore, the geometric distribution of the actual ADS-B ground stations in the CNS/ATM system is evaluated using horizontal dilution of precision for a specific flight information region. Overall, the results of the actual flight data show that it is highly feasible to apply the ADS-B radio link as an APNT solution.

In this talk I will address aviation security and secure surveillance/control that are crucial for the safety of air traffic. Two wireless systems widely deployed in this context are the Global Positioning System (GPS) and ADS-B (Automatic Dependent Surveillance Broadcast). I will discuss why wireless systems like GPS and ADS-B are hard to fully secure and I will present novel mechanisms to improve air-traffic-related security at the intersection of these two systems. In particular, I will talk about (1) Crowd-GPS-Sec, a system to detect and localize GPS spoofing attacks on moving airborne targets such as UAVs or commercial airliners, as well as (2) ADS-B-Trust, an approach to leverage machine learning for ADS-B and GPS attack detection. These systems demonstrate the potential of sensor-based crowdsourcing and wireless witnessing for attack detection purposes in a typically rather closed industry sector.

Technological independence is important for every country in global competition. One of the technological advancements developed by the Indonesian government through BPPT is Automatic Dependent Surveillance Broadcast (ADS-B) type in, which functions as a flight monitoring system. The Indonesian Air Force (TNI AU) through Depohar 50 (Depot Maintenance 50) has also developed ADS-B type in, which is currently deployed in six radar units known as the Flight Monitoring System (FMS) Mode-S. Accordance with government regulations (Minister of Transportation Regulation No. 81 of 2017), the government has mandated the use of ADS-B for flight safety. ADS-B has two types, in and out. In the TNI AU, ADS-B out is installed on aircraft such as Grob, KT-1, and Cessna, while ADS-B in is installed on Weibel radar. However, since ADS-B is not yet installed on TNI AU aircraft, flight data from the Air Traffic Control (ATC) was communicated to the pilots, hence, they can be aware of the air traffic situation. Furthermore, due to the increasing air traffic, communication with ATC has become limited. A qualitative method was used in the development of the ADS-B type in TNI AU aircraft, and the previous ADS-B type in development could only be installed on the ground station. The information received from ADS-B on the aircraft was real-time air traffic, which helps pilots make decisions to enhance flight safety, such as reducing the risk of aircraft collisions. The use of this technology on TNI AU aircraft is important for improving flight safety and technological independence.

Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup flight (lidar, EO/IR) or ground (radar, EO/IR) data to safely land an ALTA6 or other test UAS. Second, a series of Sense-and-Avoid (SAA) experiments between our test UAS and a tethered aerial obstacle. In the future, we will use this experience with fail-safe, fail-secure methods and software to further test more advanced scenarios between multiple UAS. The network of Drone Net instruments in a local area as well as regional and more global cloud-based networks will allow for heterogeneous information fusion and algorithm development for multi-sensor drone detection, classification, and identification with more accuracy than a single database or sensor system. The latent power of the Drone Net project is in its open-design ground and flight sensor network with data sharing capability to improve data mining and machine learning over time for analysis and security applications. More concrete applications for Drone Net include UTM integration with ATC and UAM, but also airspace safety and security for campus and public venues in general. Herein, we will present our experimental design and current test data from Embry-Riddle Aeronautical University in collaboration with University of Colorado Boulder toward completing the open specification for ground and flight instruments. We would additionally like to invite others to participate in growing the network and the data available for UTM, UAM, and GA shared airspace research.

ADS-B (Automatic Dependent Surveillance— Broadcast) is an emerging means of aeronautical surveillance for air traffic control (ATC) where aircraft periodically report their positions to the ground. Although ADS-B can provide a better positional accuracy and update rate, anomaly position report has been concerned. Therefore, the reported positions should be verified at the ground before being provided to ATC applications. This paper proposes a position verification method combining angle-of-arrival (AOA) and time-difference-of-arrival (TDOA). The proposed method was evaluated by numerical simulations. As a result, reduction of miss detections was observed.

Air traffic control nowadays mainly relies on radar based surveillance system and with the growth of air traffic, the problems of air transportation system is approaching its limit. The increase in the task of scheduling flights results in higher overall operation cost and pose higher rate of flight safety hazards. Thus, an upgrade from conventional air traffic management systems to the Next Generation Air Transportation (NextGen) System is required. The Automatic Dependent Surveillance Broadcast (ADS-B) modem is the key component of NextGen, which uses Global Navigation Satellite System (GNSS) to provide better accuracy of aircraft position. ADS-B continuously broadcasts information on altitude, position, identity, velocity, and other aircraft information to generate best picture for air traffic management. In this paper, the ADS-B modulator and demodulator have been developed using high-level RVC-CAL dataflow actor language, and automatically synthesized to C and Verilog/VHDL codes for hardware and software implementations. Results show that the performance of the proposed designs at high-level exceeds the minimum throughput requirement specified in the ADS-B standard by a factor of more than three.

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace.