Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna

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.

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.

Satellite-based automatic dependent surveillance broadcast (ADS-B) is of great importance in the next generation air traffic management system. However, there are two major challenges: interference and low signal to noise ratio (SNR). The interference can be mitigated by various separation algorithms. In this paper, we are committed to improving the detection performance for the ADS-B signals with downlink format field equal to 17 at low SNRs. At the moment, commercial receivers work well with sufficient SNRs but may not function properly in the case of the low SNRs. To this end, based on the characteristics of the ADS-B signal frame format, we propose a novel multi-criteria preamble detection algorithm. The proposed algorithm is a constant false alarm rate (CFAR) detection algorithm, and the determination of threshold is independent of noise power. We make a detailed theoretical analysis of each criterion, and develop the analytic expression of the detection probability. All the theoretical results are validated by the Monte Carlo simulations. The simulation shows that the proposed algorithm has an excellent probability of detection when the SNR is low, which is suitable for the satellite-based ADS-B application.

As the Automatic Dependent Surveillance-Broadcast (ADS-B) system has gained wide acceptance, additional exploitations of the radioed satellite-based information are topics of current interest. One such opportunity includes the augmentation of the communication ADS-B signal with a random biphase modulation for concurrent use as a radar signal. This paper addresses the formulation and analysis of a suitable noncooperative multitarget tracking method for the ADS-B radar system using radar ranging techniques and particle filter algorithms. In addition, the low-update-rate measurement due to the ADS-B system specification is discussed in order to provide acceptable estimation results. Simulation results show satisfactory tracking capability up to several kilometers with acceptable accuracy.

To meet the future needs of increasingly crowded airspace, the International Civil Aviation Organization (ICAO ) proposed to use the Automatic Dependent Surveillance-Broadcast (ADS-B ) to provide navigation and surveillance technology to solve the problems of security and capacity in the airspace. But ADS-B does not offer any authentication and encryption. So it is vulnerable to attacks by various illegal devices. A novel radiofrequency fingerprint (RFF ) recognition method of aircraft identity verification based on deep learning is proposed. The ADS-B signal captured by RTL-SDR is used for confirmation. The experimental results show that the fingerprint is called the Contour Stellar Images with a better recognition effect under different networks and different SNR.

In this paper, we present a fun way to teach digital communications in engineering schools. Through Software Defined Radios (SDR), we propose to the students of the Telecommunications Department of the ENSEIRB-MATMECA, Bordeaux INP to put into practice their skills in digital communications, signal processing, channel coding, etc. on real signals. More precisely, we guide them through a project, to decode the trajectories of aircraft by demodulating Automatic Dependent Surveillance-Broadcast (ADS-B) signals. In addition to the playfulness of this project, dealing with real signals also makes it possible to verify certain modelling assumptions such as the white and Gaussian noise character or the frequency non-selectivity of the channel disturbing the ADS-B signals. The novelty of our approach also lies on the collaborative aspect of this project. Students can test their implementation any time in class or at home by sharing with us their works which is merged on an always online server connected to a SDR hardware.

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

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.

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.

Automatic Dependent Surveillance-Broadcast (ADS-B) is an International Civil Aviation Organization (ICAO) formally identified and promoted mainstream surveillance methods of the future. However, the increasing airspace traffic density makes overlapping a serious problem. In order to separate the overlapped ADS-B signals in time domain, orthogonal projection algorithms are carried out for different antenna situations. Firstly, Singular Value Decomposition (SVD) is used to determine the position of overlapped signals. Then, for antenna arrays, Projection Algorithm (PA) is used to separate the overlapped signals. For single antenna, Projection Algorithm Single Antenna (PASA) is used. Last, the separated signals are compared and verified.

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.

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.

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.

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.

To improve the efficiency, safety, and capacity of air space system, an Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the popular technologies used in air traffic surveillance.The ADS-B uses 1090 MHz band.ADS-B is complemented by existing radar-based technologies to locate aircraft.The coexistence of radar systems and ADS-B is a key system to detect and resolve conflicts in the Next Generation Air Transportation System (NGATS).But, the major disadvantage of ADS-B is its implementation complexity and increase in cost without user-friendly.This paper focuses to reduce the complexity and cost of ADS-B system implementation in MATLAB with the help of Software Defined Radio (SDR).SDR is user-friendly and easy to handle aircraft information without increasing the cost.The ADS-B system implemented with SDR can receive the multiple numbers of aircraft information such as altitude, latitude, longitude, speed, and direction in real-time.Usage of SDR maximizes the information coverage with reliability and can achieve timely communication in Air Traffic Control (ATC) networks.