Air Traffic Surveillance Using IP-Based Space Information Network

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%.

PurposeThe purpose of this paper is to study the various solutions and recommendations provided by researchers in applying the blockchain concept to different problems in aviation industry. It will discuss and highlight the specific approaches that leverages blockchain to mitigate the automatic dependent surveillance-broadcast (ADS-B) security issues. Furthermore, it introduces an innovative design and method to secure ADS-B data using a tamper-resistant distributed public ledger of authenticated flight plans and validates the position and other parameters of the associated aircraft identifier against the flight paths/routes that are stored in the blockchain ledger.Design/methodology/approachADS-B is the key technology that is mandated by Federal Aviation Administration in USA by 2020. However, ADS-B data is neither encrypted nor authenticated. This paper proposes a novel solution using blockchain to secure the ADS-B data communications and in-depth analysis of existing solutions covering the following aspects: classification of various possible attacks on ADS-B. Presents various solutions proposed by different researchers regarding use of blockchain in aviation industry. Discuss a new solution to secure ADS-B using blockchain. Discuss the high-level architectural framework of the proposed and patented solution. Finally, presents the conclusions and future work scope.FindingsWhile the main intention of this paper is to bring together all the existing solutions using blockchain to secure aviation and ADS-B data at one place, the proposed novel solution could contribute to maintaining security and privacy for aircrafts flying in the airspace at any point in time. Continuously securing the ADS-B data transmissions based upon the filed flight plans in real time can provide a mechanism to identify spoofed aircraft messages and communicate the same to ground stations for authentication of existence of such a malicious aircraft. Thus, this solution also differs from all the existing ones.Practical implicationsAs aviation industry is in its infancy stage in implementing blockchain-based solutions, practical implementation of the proposed concept might take longer.Originality/valueThis paper is a comprehensive survey and review paper. To the best of the authors’ knowledge, this is the first of its kind that presents various use cases for usage of blockchain in aviation industry along with a detailed review of existing proposed or implemented solutions using blockchain to secure ADS-B data. This can serve as an invaluable reference for the future researchers on this topic in both industry and academia.

Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.

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.

Automatic dependent surveillance-broadcast (ADS-B) is a system in which aircraft continually transmit their identity and GPS-derived navigational information. ADS-B networks for air traffic monitoring have already been implemented in areas around the world, but ground stations cannot be installed in midocean and are difficult to maintain in the Arctic, leaving a coverage gap for oceanic and high latitude airspace. A potential solution for worldwide tracking of aircraft is through the monitoring of aircraft-transmitted ADS-B signals using satellite-borne receivers. To investigate this possibility, a high altitude balloon experiment was carried out in June 2009 to determine if ADS-B signals can be detected from near space. The Flying Laboratory for the Observation of ADS-B Transmissions (FLOAT) was the first stratospheric platform to collect ADS-B data. The FLOAT mission successfully demonstrated the reception of ADS-B signals from near space, paving the way to the development of a space-based ADS-B system.

Automatic Dependent Surveillance-Broadcast (ADS-B) is an air traffic surveillance system in which aircraft broadcast GPS position, velocity and status on 1090 MHz at random intervals between 0.4 and 0.6 seconds. ADS-B networks for air traffic monitoring have been implemented worldwide, but ground stations cannot be installed in oceanic regions, leaving these areas uncovered. A solution for tracking aircraft over the ocean is through the monitoring of ADS-B signals by using spaceborne receivers. The Royal Military College of Canada has developed an ADS-B receiver that is scheduled to fly as a technology demonstrator on the Canadian Advanced Nanospace eXperiment-7 (CanX-7) nanosatellite. The payload will collect ADS-B data over the North Atlantic that will be compared to truth data provided by air traffic services. A potential issue for the CanX-7 payload is signal collisions. The extended footprint of the satellite coverage means that a large number of aircraft may be in view at any one time, leading to ADS-B messages that arrive simultaneously at the receiver not being decoded. A simulation of CanX-7 passage over the operations area was carried out to calculate the probability of signal collisions. Using the Aloha Protocol, it was determined that the loss of information as a result of signal collisions is well within the standards of ground based radars used by air traffic system agencies.

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.

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.

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.

Summary form only given. The FAA's future aviation surveillance system is based on the use of various technological surveillance elements, each tailored to the airspace and capacity requirements within a particular geographic area. The Primary Surveillance Radar (PSR), Monopulse Secondary Surveillance Radar (MSSR), Mode S, Automatic Dependent Surveillance-Broadcast (ADS-B) and ADS-Contract (C) are technologies that can provide the surveillance capabilities required for operations in the National Airspace System (NAS) well into the future. ADS-B and multilateration are under development as technologies that will be capable of supporting the Advanced Surface Movement Guidance and Control System (A-SMGCS), by the provision of position and other aircraft/vehicle-derived data. ADS-B and/or ADS-C are suitable technologies to provide surveillance data in areas where currently no radar infrastructure exists or is not feasible. Current ground based surveillance functions (i.e., PSR, MSSR and Mode S), must provide independent verification of position information provided in the ADS-B messages, along with weather, intruders and blunder detection information. Future surveillance capabilities may be implemented to allow downlink aircraft parameters (DAP) to be used to improve ground based surveillance data processing (SDPS) systems and ATM thus allowing greater traffic capacity to be handled by the controller. In addition, ADS-B may be implemented to relieve the traffic load from the MSSR/Mode S systems where the density has slowly risen to such a level that the capacity of the secondary radar may limit future expansion.

The European Air Traffic Management (ATM) system is moving to a net centric operational environment. This transformation increasingly depends on appropriate communication, navigation and surveillance technologies (CNS) to enhance safety, efficiency, and capacity. New aeronautical data link communications systems such as Automatic Dependent Surveillance Broadcast (ADS-B) allows safer operations, denser airspace and cost savings for operators. However, it is a fact that ADS-B provides unauthenticated information over an unencrypted data link. In this paper, the Mode S data link structure and broadcasts are elaborated as basis to provide a comprehensive understanding for the information exchange and cyber security analysis of ADS-B. The information exchange is demonstrated by analyzing interrogated Mode S broadcasts, followed by a cyber security analysis in terms of a threat picture and taxonomy for various possible attacks. Finally, a scenario-based dynamic threat analysis is illustrated to clarify the consequences of ADS-B vulnerabilities.

Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.

The emergence of Automatic Dependent Surveillance-Broadcast (ADS-B) caused a revolution in Air Traffic Control (ATC) thanks to the numerous benefits this protocol provides, especially when compared to traditional surveillance technologies. Notably, ADS-B offers improved location accuracy from its use of GPS while reducing operational and maintenance costs. Despite these advantages, ADS-B's open broadcast of clear-text messages resulted in several vulnerabilities that negatively affected its widespread usage. In previous work, we proposed ADS-Bsec as a framework that leverages technologies such as cryptography, location verification and artificial intelligence to address the core of such vulnerabilities while keeping compatibility with the original protocol. However, air traffic services require aircraft crossing different authorities' zones to obtain additional authorization tokens, especially during international flights, thus imposing the need for ADS-Bsec to have a key management scheme that addresses this added layer of complexity. This paper presents a solution for this issue that extends the ADS-Bsec framework with the proposed implementation of a key management module based on our prior work on the key exchange protocol. The proposed module exchanges and manages keys to be used in the message verification process for each Flight Information Region (FIR) the aircraft goes through during its flight. We evaluate our ideas with a simulation of a real-world scenario based on actual data from a flight departing New York JFK airport and landing at Los Angeles LAX airport. We provide an analysis and a discussion of the results.

Automatic dependent surveillance-broadcast (ADS-B) is a very important communication and surveillance technology in air traffic control (ATC). In the future, more and more satellites will carry out ADS-B technology to perform a global coverage. In order to make full use of the resources in the satellite, this paper proposes a solution for satellite three-axis attitude determination using the ADS-B receiver. The principle of ADS-B-based attitude determination is presented first. On this basis, ADS-B-based methods are employed to solve the problem. To achieve a higher attitude determination precision, gyro is combined with the ADS-B receiver using a multiplicative extended Kalman filter (MEKF). Finally, a simulation is carried out and the result is presented.

The International Civil Aviation Organization (ICAO) has decided to adopt Communications, Navigation, and Surveillance/Air Traffic Management (CNS/ATM) as the 21st century standard for navigation. Accordingly, ICAO members have provided an impetus to develop related technology and build sufficient infrastructure. For aviation surveillance with CNS/ATM, Ground-Based Augmentation System (GBAS), Automatic Dependent Surveillance-Broadcast (ADS-B), multilateration (MLAT) and wide-area multilateration (WAM) systems are being established. These sensors can track aircraft positions more accurately than existing radar and can compensate for the blind spots in aircraft surveillance. In this paper, we applied a novel sensor fusion method with Interacting Multiple Model (IMM) filter to GBAS, ADS-B, MLAT, and WAM data in order to improve the reliability of the aircraft position. Results of performance analysis show that the position accuracy is improved by the proposed sensor fusion method with the IMM filter.