Automatic Dependent Surveillance-Broadcast Signals Research Articles

Blockchain-based UAV Continuous Authentication

With the growing utilization of unmanned aerial vehicles (UAVs), low-altitude airspace is becoming increasingly congested, posing significant challenges to Air Traffic Control (ATC). The Automatic Dependent Surveillance-Broadcast (ADS-B) system is used to report the location of UAVs. However, due to channel noise and adversarial interference, ADS-B data may be inaccurate. To address this issue, a position estimation method based on Bidirectional Long Short-Term Memory Multi-Layer Perceptron (BiLSTM-MLP) was proposed. In this method, ADS-B signal power is used to estimate the position of UAVs as a baseline approach. Experimental results demonstrate that the BiLSTM-MLP based method significantly improves estimation accuracy. Moreover, a blockchain-based dynamic reputation mechanism, aligned with the position estimation method, was proposed. The mechanism integrates information from the third-party UAV and the location data provided by the UAV itself to dynamically update the credibility of the UAV, and thus achieves decentralized access control of UAVs in low-altitude airspace.

Independently convolutional gated recurrent neural unit 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.

Assessing Automatic Dependent Surveillance-Broadcast Signal Quality for Airplane Departure Using Random Forest Algorithm

This study aims to assess the safety level of the Automatic Dependent Surveillance-Broadcast (ADS-B) signal quality during airplane departures at Sultan Mahmud Badaruddin II Airport. The Aero-track application was utilized to monitor commercial aircraft departures and collect observation data. The collected data underwent processing using data analysis algorithms and labeling processes, resulting in a comprehensive dataset for evaluating ADS-B signal quality. Signal quality was categorized into four levels, and a model was built using the Random Forest algorithm, achieving an accuracy of 99%. Comparative analysis with SVM and Naive Bayes algorithms showed accuracy values of 93% and 97% respectively. Consequently, the Random Forest Model was chosen for estimating ADS-B signal quality during commercial aircraft takeoff and landing.

An ADS‐B signal poisoning method based on generative adversarial network

Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. An ADS-B signal poisoning method based on Generative Adversarial Network is proposed. This method can generate poisoned signals. One of ADS-B signal classification networks is assigned as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will be recognized incorrectly by the attacked network while classified correctly by the protected network. An attack-protect-similar loss function is further proposed to achieve ‘triple-win’ in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show that the attacked network classifies poisoned signals with 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.

Single antenna ADS-B overlapping signals separation based on deep learning

As a new type of surveillance technology, Automatic Dependent Surveillance-Broadcast (ADS-B) has been regarded as the cornerstone of the next generation air surveillance. However, a potential problem is that multiple ADS-B signals may overlap when they come from different aircraft simultaneously. This paper explores the possibility of using deep learning to separate pulsed ADS-B overlapping signals. Most existing deep learning algorithms separate the overlapping signals by utilizing the difference in time-frequency domains. But all ADS-B signals are transmitted at the same frequency, those deep learning methods based on time-frequency analysis are ineffective. This paper separates pulsed ADS-B overlapping signals directly in the time domain. Firstly, an encoder is used to extract the mixed features of ADS-B overlapping signals in the time domain. Then, Temporal Convolutional Network (TCN) is used to extract the non-overlapping ADS-B signal feature from the output of encoder. Finally, the separated time-domain signals are reconstructed by a decoder. Simulation results show that the separation accuracy of the algorithm based on deep learning is higher than that of the traditional algorithm.

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

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.

Cybersecurity Attacks on Software Logic and Error Handling Within ADS-B Implementations: Systematic Testing of Resilience and Countermeasures

Automatic-dependent surveillance-broadcast (ADS-B) is a cornerstone of the next-generation digital sky and is now mandated in several countries. However, there have been many reports of serious security vulnerabilities in the ADS-B architecture. In this article, we demonstrate and evaluate the impact of multiple cyberattacks on ADS-B via remote radio frequency links that affected various network, processing, and display subsystems used within the ADS-B ecosystem. Overall we implemented and tested 12 cyberattacks on ADS-B in a controlled environment, out of which 5 attacks were presented or implemented for the first time. For all these attacks, we developed a unique testbed that consists of 36 tested configurations. Each of the attacks was successful on various subsets of the tested configurations. In some attacks, we discovered wide qualitative variations and discrepancies in how particular configurations react to and treat ADS-B inputs that contain errors or contradicting flight information, with the main culprit almost always being the software implementation. In some other attacks, we managed to cause denial of service by remotely crashing/impacting more than 50% of the test set that corresponded to those attacks. We also implemented, and report some practical countermeasures to these attacks. We demonstrated that the strong relationship between the received signal strength and the distance-to-emitter might help verify the aircraft.s advertised ADS-B position and distance. For example, our best machine learning models achieved 90% accuracy in detecting attackers' spoofed ADS-B signals.

Hybrid N-Inception-LSTM-Based Aircraft Coordinate Prediction Method for Secure Air Traffic

With the rapid growth of the number of flights, the traditional radar system has been unable to meet the needs of flight supervision. At the same time, it also puts forward higher requirements for air traffic management (ATM). Automatic dependent surveillance-broadcast (ADS-B) is a promising technology in the next generation of air traffic control (ATC). However, the openness of ADS-B system brings the opportunities for terrorists to tamper with data. In this paper, we propose a novel aircraft coordinate prediction hybrid model based on deep learning. The proposed model combines inception modules and long short-term memory (LSTM) modules. Inception modules are used to extract the spatial features of dataset, and LSTM modules are used to extract the temporal features of dataset. In addition, we use the ADS-B signal strength instead of its specific information to obtain aircraft coordinates. Signal strength is not easily tampered with, but it carries limited information. Therefore, this scheme sacrifices a certain precision for reliability. Inception modules and LSTM modules are combined in different ways to perform experiments on the real-world ADS-B datasets from OpenSky network. The experimental results show that the proposed 2-Inception-LSTM is the local optimal model. The prediction error is within 10 km. It can be suitable for situations where the positioning accuracy of aircraft coordinates is not pursued, but the positioning reliability must be guaranteed.

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.

Rancang Bangun Monitoring Lokasi Pesawat Menggunakan ADS-B dengan RTL-SDR dan Raspberry Pi

— Automatic Dependent Surveillance Broadcast (ADS-B) is a surveillance technology that provides information on aircraft in the air in the form of 24 bit ICAO aircraft address, ident or squawk, massage, altitude, nationality, speed, longitude, track and heading. The problem faced now is that surveillance can only be done with a web and android-based application on FlightRadar24 so that if the user wants to display more aircraft information, the user is required to pay a subscription. To overcome this problem, hardware is needed that can receive ADS-B signals with a frequency of 1090 MHz and can translate them into information signals. RTL-SDR is hardware that can receive signals with a frequency range from 25 MHz - 1700 MHz, by applying the Raspberry Pi it is used to configure RTL-SDR as a receiver capable of receiving information from ADS-B signals. To get the maximum reception, an omnidirectional antenna is needed that can receive signals from all directions. With this system, it is expected to make it easier to monitor aircraft in real time and processing ADS-B signal data is determined by the strength and weakness of the signal that can be received by RTL-SDR.

Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

Signal Separation for Automatic Dependent Surveillance-Broadcast Using Improved Single Antenna Project Algorithm

The Automatic Dependent Surveillance-Broadcast (ADS-B), as a new surveillance technology, is being vigorously promoted by International Civil Aviation Organization (ICAO). However, overlapping among multiple signals is inevitable because of the randomness of ADS-B signal transmission. An improved Project Algorithm Single Antenna (PASA) algorithm which separates the overlapping signals with one single channel is proposed. Firstly, a new matrix reconstruction method for the data that received by single channel is proposed to decrease the requirement of relative time delay and frequency difference between two ADS-B signals, and then the overlapping signals can be separated by utilizing the projection algorithm. The effectiveness of the algorithm is verified by simulation experiments.

Rancang Bangun Receiver Sinyal ADS-B Pesawat Menggunakan RTL-SDR serta Antena 1090 MHz

Automatic Dependent Surveillance Broadcast (ADS-B) is a surveillance technology used in air navigation. This technology can view information in the form of 24 bit ICAO aircraft addresses, ident or squawk, latitude, altitude, nationality, speed, longitude, track and heading. The current development problem can only be done with the Flight Radar24 web-based application, so it requires an internet connection to operate. To solve this problem, the necessary hardware is needed that can receive ADS-B signal with a frequency of 1090MHz and can translate it into information. The RTL-SDR R820T2 is a software-based hardware device that can receive a wide range of signals from 25 MHz - 1700MHz. To maximize signal reception, an additional omnidirectional antenna is capable of receiving signals from all directions. With this system, it is expected to make it easier to fly directly without an internet connection.

Modeling and evaluation of enhanced reception techniques for ADS-B signals in high interference environments

The RTCA (Radio Technical Commission for Aeronautics) DO-260B document has stimulated in latest years the adoption of novel algorithms to improve the ability of receivers to detect and decode ADS-B (Automatic Dependent Surveillance-Broadcast) signals, with particular emphasis on preamble detection, declaration of bit state and confidence level, detection and correction of errors. Motivated by an industry-driven research project, this paper reports on performance analysis of a set of enhanced techniques that we have implemented for detection and decoding of ADS-B signals transmitted at 1090 MHz carrier frequency also in high interference conditions. An extensive set of results has been derived through a specifically developed simulation environment, able to closely model a wide range of operational scenarios by generating ADS-B frames and Mode A/C interfering signals. The simulation environment also includes an implementation of two RF (Radio Frequency) receiver chains that refer to a linear amplifier and a logarithmic amplifier, respectively. The investigations are mainly focused on evaluating the impact of the interference generated by Mode A/C replies on preamble detection and data decoding for ADS-B signals. In this regard, the ability of enhanced reception techniques to improve detection performance is assessed. Numerical results obtained from extensive simulation runs are reported and commented. They actually demonstrate that enhanced techniques are essential to guarantee adequate detection performance in the harsh environments envisaged by increased intensity of air traffic and the extensive use of ADS-B for traffic control.

Specific emitter identification of radar based on one dimensional convolution neural network

Specific Emitter Identification (SEI) of radar has become a focus point of research and difficulty in the field of electronic reconnaissance, and the application of Automatic Dependent Surveillance Broadcast (ADS-B) signal in Identification Friend or Foe (IFF) system is beginning to raise increasing concern. At present, the research of deep neural network in the field of target recognition mostly focuses on the modulation pattern recognition of signal, but there are few applications for the specific emitter identification. In order to overcome the shortcoming that the effect of the traditional method is not excellent, a radar emitter target recognition method based on one-dimensional(1D) convolutional neural network is proposed for ADS-B signal in this paper. Taking the sequence data of ADS-B pulse signals as the training and testing samples of emitter target identification, the frequency domain features of ADS-B signal are obtained by Fast Fourier Transform (FFT), and the target identification is achieved by combining the convolution neural network with the fine feature extraction. The result of simulation demonstrates that the specific emitter identification based on frequency domain and one-dimensional(1D) convolutional neural network has an excellent recognition performance, which effectively resolves the shortcomings of the traditional method of feature extraction and low classification accuracy.

A Study on the Collision Probability of Satellite-Based ADS-B Messages Based on Homogeneous Poisson Process

Automatic Dependent Surveillance-Broadcast (ADS-B) is an air traffic surveillance system in which aircraft broadcast GPS position, velocity and status on 1090 MHz. Nowadays, as a valid alternative to SSR, ADS-B is the main technology for Air Traffic Management (ATM). With the rapid growth of airspace traffic and disappearance of MH370, satellite-based ADS-B is proposed to realize aerial surveillance globally. The satellite-based ADS-B has a series of new features that Terrestrial based does not have including wider coverage, lower SNR. These new features make ADS-B signal collisions at the receiver more severe. This paper is to analyze the collision probability of satellite-based ADS-B messages based on homogeneous Poisson process. According to the theoretical derivation and simulation analysis, a conclusion can be drawn that the collisions of two and three messages dominate in all cases. It helps in guiding the design of the hardware and software of satellite-based ADS-B receiver to focus on the separation of two and/or three collision messages only, which will dramatically reduce the complexity of the system and the load of satellite.

Novel confidence level labelling methods for ADS‐B signals with coherent demodulation scheme

In the satellite-based application, low signal-to-noise ratio (SNR) is a considerable obstacle for automatic dependent surveillance-broadcast (ADS-B), leading to some erroneous bit declarations. In this case, the confidence level of each bit should be declared legitimately to make full use of error correction. In this study, the authors focus on methods to optimise the confidence declaration for ADS-B signals with coherent demodulation scheme. A novel optimal-threshold labelling method is firstly proposed for known and unknown amplitudes. They also present a dynamic-threshold labelling method, in which a specific threshold is unnecessary. Detailed theoretical analyses are carried out for the proposed methods and are verified by simulations. Finally, they make a comparison between different confidence level labelling methods in terms of reception performance and complexity. A conclusion is set forth that the methods have a high probability of correct reception at low SNRs.

Opportunistic-Target-Measurement-Based Narrowband Statistical Modeling of Civil Aviation Surveillance Signal at 1090 MHz

Automatic dependent surveillance—broadcast (ADS-B) is one of the next-generation aeronautical surveillance systems for air traffic control. ADS-B requires an aircraft to periodically broadcast its own position to other aircraft and ground stations, thereby enabling high-performance surveillance. In this study, the received signal strength (RSS) of the ADS-B signal was measured and characterized for opportunistic flights. The RSS distribution for a single aircraft was modeled as a sum of three components: nominal RSS, bias in the equivalent isotropically radiated power (EIRP), and fading. Then, the RSS distribution for multiple aircraft was defined, considering that the EIRP bias and fading parameter are different for different aircraft. To this end, a composite distribution was employed, the parameters of which were estimated from the bias and fading statistics. Furthermore, a practical approximation of the model was proposed. The proposed model is suitable for large-scale data, enabling the realization of aircraft-by-aircraft analysis. Also, it can provide a clear explanation of the mechanism by which the RSS distribution is formed.

Real-time decoding of automatic dependent surveillance – Broadcast signals by using software-defined radio and MATLAB®

In telecommunications engineering degrees, simulations are used to understand better theoretical concepts. However, in courses related to digital communications or signal processing usually these simulations cannot be easily applied to real data or in real scenarios. This limitation does not allow the students to verify that the learned theoretical hypotheses are valid in practice. Nowadays, software defined radio platforms have been proposed to validate theoretical telecommunications concepts in real situations. Therefore, by combining a software defined radio and simulation tools, students can implement the learned notions over real data in real scenarios. This allows students to work under real applications in order to learn by experience and to see that the theoretical concepts are valid in the real world. This paper presents a project involving real-time decoding of automatic dependent surveillance–broadcast (ADS-B) signals, implemented by using a universal software radio peripheral software defined radio and MATLAB®. The universal software radio peripheral allows to receive real automatic dependent surveillance–broadcast signals sent by the aircraft. Then, by using MATLAB® tools, the automatic dependent surveillance–broadcast baseband signals are decoded. The students are guided through a manual to demodulate the real automatic dependent surveillance–broadcast signals and display in a map the aircraft trajectories. The evaluation is based on a competition between the students, considering execution time of their algorithms, and a final report of the project. This project allows to obtain a game-oriented and novel approach for teaching digital communications and signal processing. The implementation of this project in telecommunications engineering courses motivate the students, allowing them to understand better difficult theoretical concepts, and improve their computer, teamwork, and collaboration skills. The originality of the approach is based on the usage of a prototyping platform, combined with simulation tools, for practical application of telecommunications engineering concepts using real signals.

CUDA-Based Method to Boost Target Performance Evaluation of Space Systems for Automatic Mobile Object Identification and Localization

The complex organization and application conditions of space systems for automatic identification and localization of mobile objects, which include the automatic identification system (AIS) and automatic dependent surveillance-broadcast (ADS-B) system, determine the choice of simulation models for the mathematical formalization of their operation. Simulation modeling of satellite constellations capable of receiving, processing, and retransmitting AIS and ADS-B signals can take a significant amount of time when being used to substantiate circuit design solutions for satellites and plans for their further application given a large number of radiation sources to be simulated (e.g., for the AIS, their number exceeds 500 thousand). One of the methods for solving this problem is parallel computing based on the compute unified device architecture (CUDA) technology. However, due to the specificity of machine instruction execution on NVIDIA GPUs, software quality depends heavily on GPU memory allocation efficiency and algorithms for program code execution. In this paper, we propose a method for target performance evaluation of space systems for automatic identification and localization of mobile objects; the method uses massively parallel computations on GPUs to provide a significant reduction in simulation time, which is especially important for multi-satellite constellations. The efficiency of the method is confirmed by model-cybernetic experiments carried out on various software and hardware platforms.