Study of ADS-B Data Evaluation

Examining visitors’ spatiotemporal movement patterns within parks and protected areas (PPAs) has become an informative methodology for management actions and understanding visitor behavior. This work has given managers and researchers a better understanding of visitor spatial behaviors, spatiotemporal resource impacts, and theoretical and organizing frameworks for approaching this type of research. However, few studies have examined the spatial patterns of air traffic, specifically low-level air tours, above PPAs and the resulting impacts to visitors’ experience and the natural soundscapes. This need is particularly relevant in the U.S. due to legislative mandates that the National Park Service (NPS) and Federal Aviation Administration (FAA) have to manage air tours over NPS units. Further, prior studies have shown how air tours negatively impact both visitors’ experience and the natural soundscapes. Understanding the spatiotemporal patterns of air tours is critical for the managers working with air tour operators and the FAA for planning, ensuring compliance with management decisions, and examining the effects of aircraft noise on PPA resources and the on-the-ground visitors. Recently enacted laws and regulations across the globe, including the U.S., are requiring all aircraft operating in controlled airspace to equip air traffic tracking technology called Automatic Dependent Surveillance-Broadcast (ADS-B) Out avionics. ADS-B Out broadcasts an unencrypted and publicly accessible signal that relays latitude, longitude, altitude, and unique identification code. In the U.S., this code can then be cross-referenced to a publicly accessible database with information including the type/model of aircraft and the registered owner. Only a single prior study has examined ADS-B data over NPS units, finding both potential and limitations to this new technology for tracking air tours. This study seeks to expand methodological improvements and ADS-B’s potential to inform management actions by exploring ADS-B data at Haleakalā National Park (HALE). This study explores the use of ADS-B data at HALE for monitoring compliance with spatially explicit conditions within a preexisting agreement established between the park and air tour operators. The study identified 321 air tours, compared with 454 air tours reported by operators over the same time period. Compliance with the spatially explicit agreement conditions was generally high, though better for lateral offsets than minimum altitude requirements. Overall, this study advances methodological uses of ADS-B logging to track air tours over PPAs and advances knowledge about the potential utility and current limitations of using ADS-B tracking systems for compliance in PPA contexts. Subscribe to JPRA

Automatic Dependent Surveillance-Broadcast (ADS-B) technology offers significant advantages in civil aviation applications, including high update rates, low construction and maintenance costs, and rich data content. However, its heavy dependence on the Global Navigation Satellite System (GNSS) renders it vulnerable to GPS interference, which can severely degrade data quality. To address this issue, it is necessary to optimize flight trajectories by incorporating feature values derived from ADS-B data. This paper proposes a flight path optimization method based on the ADS-B Navigation Integrity Category (NIC). First, features are extracted by analyzing the downlink data structure of ADS-B to filter out flight path points with low NIC values. Subsequently, radar data is dynamically integrated with the refined ADS-B data to enhance trajectory continuity during GPS interference events. Finally, the method is validated using real flight data, which also aids in the identification of GPS interference sources. The findings of this study can be applied to improve the quality of flight trajectories, trace the origins of GPS interference, optimize air traffic management efficiency, and significantly enhance aviation safety.

One of the main values of utilizing automatic dependent surveillance - broadcast (ADS-B) data for air traffic control is that it provides greatly improved coverage as well as improved accuracy. As with all technology advances and deployments, especially in the National Airspace System (NAS) domain, there is a long transition time when the old and new systems must work together. The goal of the virtual radar is to provide ADS-B data in a way that completely emulates the characteristics of the current physical radars such that any automation system receiving the data will not have to be modified. Some of the issues that must be addressed include accuracy differences, conversion from cartesian GPS coordinates to polar range-azimuth coordinates, update frequency, providing a conversion of the of the ADS-B data to common digitizer (CD) beacon messages, generating the beacon real time quality control (BRTQC) and search real time quality control (SRTQC) messages as well as the radar status messages. Update frequency has unique issues to be resolved in the fact that the timing of the ADS-B reports may not correspond with the time that virtual radar beam may have hit the target. This issue of time registration will be investigated and the approaches to solving them discussed. The test and validation of virtual radars may require some new procedures and test tools to adequately insure the accuracy and correct operation. The benefits of virtual radars are exciting as they allow all of the benefits of ADS-B, improved coverage, and accuracy, and less maintenance without having to wait for upgraded automation systems.

A-SMGCS is an airport system that is used to improve the efficiency of using runways, apron and taxiways by maximizing runway capacities in all situations and weather while maintaining the required level of security. One of the parameters that becomes a requirement in the A-SMGCS system is the Probabillity of Target Report (PTR) whose value must be greater or equal to 95% in the area maneuver with update rate of the data one second [1]. Automatic Dependent SurveillanceBroadcast (ADS-B) is one of the cooperative sensors in the A-SMGCS system. The ADS-B sensor is a receiver that is used to capture ADS-B data from aircraft or vehicles equipped with ADS-B transmitters. The amount of ADSB data received by the ADS-B receiver and is needed for the A-SMGCS system is one of the factors that determine the Probability of Target Report (PTR). This paper will test three ADS-B receivers which will be selected for use as a cooperative sensor in the next A-SMGCS development. From the results of analysis and retrieval of data obtained by Radarcape ADS-B receiver more suitable for the development of the A-SMGCS system.

In-Trail Procedures (ITP) is an application of Automatic Dependent Surveillance-Broadcast (ADS-B) that allows aircraft to change flight levels in areas where current non-radar separation standards would prevent desirable altitude changes. The ITP avionics receive ADS-B Out position reports from aircraft at intermediate flight levels that could be blocking the desired climb or descent maneuver. The most recent standard for ADS-B broadcast is RTCA/DO-260B [1]. This is also known as Version 2. Equipage is mandated by 2020 in the US for the same aircraft that carry transponders. Equipage is mandated in Europe by 2015 for forward fit aircraft and by 2017 for retrofit aircraft. Standards have been developed for the ADS-B data accuracy requirements for ITP [2, 3]. The standards are straightforward to apply to aircraft equipped with transponders certified to DO-260B [1]. The standards are less straightforward to apply for aircraft equipped with transponders certified to the earlier standards of ADS-B, which are known as Version 0 or Version 1 [4, 5]. This paper shows that crosschecking ADS-B data with Traffic Alert and Collision Avoidance System (TCAS) data is an effective mitigation technique for uncertain ADS-B horizontal position accuracy and integrity for legacy Version 0 and Version 1 ADS-B transponders.

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.

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.

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.

Aircraft passive positioning by receiving radio signals of Distance Measuring Equipment (DME) together with processing data from Automatic Dependent Surveillance Broadcast (ADS-B) about surrounding air traffic is represented in paper. Interrogation messages of other aircraft are used with ground DME network reply messages to calculate Time Difference of Arrival and for further passive aircraft coordinates estimation by hyperbolic navigation method. Communication message transmission by ADS-B is considered as a source of DME interrogators location for relative navigation approach. Accuracy assessment of hyperbolic positioning takes into account uncertainty areas of DME interrogators location. Accuracy analysis is performed with computer-based simulation with live air traffic data.

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation systems, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on Improved Generative Adversarial Network and long short-term memory networks (IGAN-LSTM). In the model, IGAN mainly improves the generator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> architecture of GAN through upgrading the commonly used encoder architecture to encoder-decoder-encoder architecture, which captures the training data distribution within both data space and latent space. IGAN then utilizes encoding losses based on latent spatial features to determine whether a data sample is anomalous or not. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experimental results show that the proposed model has a higher area under the receiver operating characteristic curve and the precision recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.

국제해사기구(International Maritime Organization, IMO)에서는 선박의 해상 충돌을 방지하기 위하여 SOLAS의 요구조건에 따라 모든 선박에 대하여 선박자동식별장치(Automatic Identification System, AIS)를 의무적으로 장착하도록 권고하고 있다. AIS를 사용하면 타선에 대한 제원 및 항행정보의 획득이 가능하여 충돌방지뿐만 아니라 광역관제, 조난 선박의 탐색구조 등 안전관리에도 활용이 가능하다. 본 논문에서는 AIS 장비를 사용하여 ADS-B (Automatic Dependent Surveillance - Broadcast) 개념 기반의 항공감시용 송수신기를 구현한 결과에 대하여 살펴보도록 한다. 기본적으로 AIS는 해상에서 사용되기 때문에 고도 정보를 사용하지 않지만, AIS 장비 내부에 사용하는 GPS (Global Positioning System) 칩셋에서 제공하는 고도 정보의 추출이 가능하다. 고도 정보를 포함한 감시 메시지 형식을 정의하고, SOTDMA (Self-Organizing Time Division Multiple Access) 방식을 개선하여 ADS-B 송수신기를 구현하였다. 적용 결과를 확인하기 위하여 지상시험 및 비행시험을 수행하였다. International Maritime Organization (IMO) recommends the installation of an Automatic Identification System (AIS) according to requirements by SOLAS to avoid maritime collision. AIS provides traffic information of other ships that may be used for maritime traffic control, SAR (Search and Rescue) and collision avoidance to apply safety management. In this paper, preliminary results to implement an aeronautical surveillance transceiver using AIS transceiver based on ADS-B concepts are described. Although altitude information is not required for AIS since the AIS is operated at MSL (Mean Sea Level), altitude information can be extracted by a GPS (Global Positioning System) chip-set in the AIS transceiver. ADS-B transceiver is implemented by defining a surveillance message format including the altitude information and modifying SOTDMA (Self-Organizing Time Division Multiple Access) protocol. Ground tests and flight tests are performed to validate the implementation results.

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.

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.

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.

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.