A Trajectory Fusion Algorithm Combining Aircraft ADS-B Feature with SSR Data

Study of ADS-B Data Evaluation

In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.

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

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.

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.

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.

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.

Technologies relying on Global Navigation Satellite Systems (GNSS) are under growing threat of signal interference, specifically jamming and spoofing. This paper investigates Automatic Dependent Surveillance Broadcast (ADS-B) signals as a viable source for the detection and classification of such deliberate GNSS disruptions, by leveraging the space-based ADSB database from ESA's PROBA-V satellite. Different types of GNSS vulnerabilities are highlighted, as well as the limitations of current detection measures. The ADS-B protocol is examined, and different methods of ADS-B based GNSS interference detection are derived. Some initial results show the feasibility of the listed approaches, highlighting regions with high overall GNSS interference, and point out future tasks to fully leverage the ADSB database for jamming and spoofing detection and mitigation.

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.

This paper proposes a new traffic alert and collision avoidance system (TCAS) for general aviation (GA) that uses automatic dependent surveillance-broadcast (ADS-B). The proposed GA-TCAS alerts the pilot of upcoming potential conflicts, which are becoming increasingly common as the number of GA movements increases. Conflict detection and avoidance uses the ADS-B and GA maneuvers. The algorithm selects the angle according to the intruder’s heading and position in order to reduce the nuisance alerts. The detection algorithm has three stages: proximate advisory, traffic advisory, and resolution advisory. The relationship between the turn angle and the horizontal miss distance (HMD) are calculated to determine a resolution when an intruder enters the separation bubble. Using the encounter models for visual flight rule (VFR) to evaluate the performance of this TCAS algorithm, the resolution should follow the VFR and prioritize in different situations. In flight simulations, the proposed GA-TCAS is tested using real ultralight flight data. The system performance is demonstrated using flight trajectories from conflict detection and resolution.

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.

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.

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.

Air-traffic optimization is an essential part of airspace operation reengineering, as the number of flights and the usage of routes increase in the world. The NextGen and SESAR projects are important initiatives that allow for more scalability and safety in air-traffic. One element of these projects is the Automatic Dependent Surveillance-Broadcast (ADS-B) system which allows airplanes to share their position and speed. The ADS-B antennas’ coverage is somewhat limited in less economically developed and oceanic areas, resulting in a lack of flight path data. This paper proposes a method based on artificial neural networks (ANN), interpolation and average computation to fill flight path data partially tracked by ADS-B antennas. While other methods are focused on one or two dimensions of the flight path, this work is focused on infilling the 4-dimensions present in the ADS-B data (latitude, longitude, altitude and ground speed). This work is useful in analyzing performance of historical flights related to limited coverage areas or in predicting flight path in air-traffic management systems (ATMs). The comparison between the real and estimated trajectories in a set of 517 flights has shown accuracy superior to 92% for the metrics distance flown, estimated burned fuel, and trajectory correlation.

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.