ADS-B Transmitter/Receiver Simulation to Decode Real Aircraft Trajectories with Software Defined Radios

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.

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.

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.

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.

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 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.

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.

The ultimate objective of Software Defined Radio (SDR) is to replace the entire analog signal processing in the wireless transceivers with digital signal processing. This will provide the flexibility, through reconfiguration or reprogramming, to enable the transceivers to work with different air-interfaces standards using a single generic hardware platform. Most of the research in SDR to date has been focused on base stations, which do not have as tight constraints in size and power consumption as handsets. However, in order to realize the promise of integrated services and global roaming capabilities, the design and development of a multi-standard SDR handset with dynamic reconfigurability is crucial. The size and power constraints imposed by handsets with limited resources pose a major design challenge. This special issue brings together 11 papers that address various aspects of this challenge from the perspectives of wireless systems, computing architectures, embedded systems and signal processing. The most important part of an SDR transceiver is the wideband analog-to-digital converter (ADC) that converts the radio frequency signal received by the antenna into digital form for further processing. The first paper, “A multi-mode sigma-delta ADC for GSM/WCDMA/WLAN applications” by Jose et. al. proposed a cascaded reconfigurable sigma-delta modulator that is able to work with three different communication standards. This design, implemented in 0.18 μm CMOS technology, exhibits excellent signal-to-noise plus distortion ratios and has reduced power consumption compared to conventional designs. After ADC, the digitized received signal will need to be separated into signals within the various bands specified by the communication standard. This is performed by the channelizer which has the highest computational requirements of the whole SDR system. The second paper, by Panicker et al., reviews the state-of-the-art filter bank channelizer techniques with low power and dynamic reconfigurability properties. The design of reconfigurable devices can be optimized using the methodology of parametrization which bring together the common aspects of a myriad number of wireless standards into a single processing procedure. In this way, the implementation of the multi-standard SDR will be greatly simplified. “Promising technique of parametrisation for reconfigurable radio, the Common Operators Technique: fundamentals and examples” by Alaus et al. presents the common operator technique for parametrization which is less standards dependent than the more conventional Common Function technique. The direct digitization of the radio frequency (RF) signal requires very high speed ADCs which is not readily available using current technology. Therefore, current SDR implementations typically down-converts the RF A. P. Vinod (*) School of Computer Engineering, Nanyang Technological University, Nanyang, Singapore e-mail: asvinod@ntu.edu.sg

Since the initiation of the Joint Tactical Radio System (JTRS) program, the concept of software defined radio (SDR) has received considerable attention and significant progress has been made. While the raison d'etre of SDR is somewhat different for military and commercial applications, they share similar challenges in implementing a true SDR. A true SDR faces numerous hardware and software challenges, many of them still to be addressed by research. In the literature concerning SDR software architecture and requirements, the technology requirements tend to be sidelined. This is mainly because, in the JTRS program, the focus is on voice and low data rate applications; also, the frequency band is limited to UHF. On the other hand, the implementation cost is less of a concern for now. However, by the emergence of advance warfare technology where it is required to transport more bits per warrior to accommodate video and telemetry data, and also by introducing initiatives similar to the JTRS to other communication systems, the technology requirements of SDR have to be revisited. The paper focuses on the hardware technology aspect of SDR. The main technical objective of SDR is a versatile reconfigurable platform, which provides interoperability. The main blocks of a true SDR are: intelligent antenna; programmable RF modules; high-performance digital-to-analog (DAC) and analog-to-digital converters (ADC); digital signal processing (DSP) techniques/technology; interconnection technology.

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.

The development of ADS-B (Automatic Dependent Surveillance-Broadcast) is based on the use of the Mode S transponders now carried by all air carrier and commuter aircraft. ADS-B aircraft broadcast aircraft positions, identity, and other information via semi-random Mode S transponder squitters. Other aircraft or ground facilities receive the squitters and the associated position and status. Squitter reception includes the detection of the Mode S 1090 MHz waveform preamble, declaration of the bit and confidence values, error detection, and error correction. The current techniques for squitter reception are based upon methods developed for use in Mode S narrow-beam interrogators and for ACAS. In both of these applications, the rate of Mode A/C fruit that is stronger than the Mode S waveform is relatively low. Extended squitter applications now include long range air-air surveillance in support of free flight. This type of surveillance is sometimes referred to as Cockpit Display of Traffic Information (CDTT). Operation of extended squitter in very high Mode A/C fruit environments has led to the need to re-evaluate squitter reception techniques to determine if improved performance is achievable. The purpose of this paper is to provide a summary of work in progress to investigate improved squitter reception techniques. Elements of improved squitter reception being investigated include: (1) the use of amplitude to improve bit and confidence declaration accuracy, (2) more capable error correction algorithms, and (3) more selective preamble detection approaches.

This study develops a hybrid positioning scheme that combines both differential time of arrival (DTOA) and angle of arrival (AOA) observations of the 1090-MHz automatic dependent surveillance-broadcast (ADS-B) signal and implements it on a wide area multilateration (WAM) test bed. The major issue when combining DTOA and AOA observations is synchronization. A synchronization design for DTOA and AOA observations is thus evaluated. The positioning algorithm that combines AOA and DTOA observations is a filter-based approach. Based on the measurement errors from actual ADS-B signals received from real flights, simulation is used to evaluate the positioning performance of the proposed 1090-MHz ADS-B WAM with the integration of AOA/DTOA approach under two scenarios, namely DTOA alone and AOA combined with DTOA.

Estimation of aircraft–receiver distance based on the received signal strength (RSS) of a civil aviation system called the Automatic Dependent Surveillance—Broadcast (ADS-B) was investigated. The estimated distance was intended to be used for position verification, which is a countermeasure against spoofing. A two-step method was proposed in order to mitigate the uncertainty issue regarding the aircraft’s equivalent isotropically radiated power (EIRP), leveraging the regularity of flight schedule. The first step is calibration, where the bias of the EIRP from a nominal value is obtained under the assumption that the ADS-B position is true. The obtained value is stored to be used in the second step, where the distance is estimated when the same aircraft reappears. The proposed method was evaluated with measurement data. As a result, distance errors with a mean of 0.8 km and a standard deviation of 18.6 km were obtained. Also, the resulting error distribution was approximated by a normal distribution.