Validation of Atmospheric Ducts Inversion Using Automatic Identification System Signals at Sea

Aimed at addressing the problem of carrier synchronization for satellite-based Automatic Identification System (AIS) signals, a novel data-aided carrier frequency offset estimation method in the intermediate frequency is proposed in this paper. AIS signals use Gaussian Minimum Shift Keying (GMSK) for transmission, and a nonlinearity of the GMSK signal is shown to be a sine wave with frequency related to the carrier frequency offset. Based on that, this method constructs an auxiliary function with the available symbols to avoid the influence of the modulated phase information, and it estimates the frequency offset due to Doppler shift with a new interpolated FFT method. Moreover, this method performs better when the signals are filtered by band pass filter even through the bandwidth is as low as the 1/3 nominal bandwidth. Computer simulations are used to assess the synchronizer performance on AWGN channels.

With the rapid development of global industry, AIS (automatic identification system) has become an important tool for ship management and safety monitoring. In order to better display the frequency distribution of AIS signals, this paper graphically displays the data in the AIS signals, allowing users to fully understand the data in the AIS signals, including ship type distribution, speed range, and signal frequencies in different areas. It uses Kafka and Flink technology to receive, clean and analyze the sent data, and finally stores it in MySQL and Redis data. It reads the data through Spring Boot + MyBatis, and uses Vue + Echarts chart tool and AutoNavi Map API for display and analysis. To gain an in-depth understanding of its importance and application prospects in the field of epidemics.

Global Navigation Satellite Systems (GNSS), today the primary mean for Position, Navigation and Timing (PNT) information on-board of a vessel, are highly vulnerable to unintentional and intentional interference like jamming or spoofing. Therefore, maritime community seeks for an alternative system to compensate this thread and follow the vision of the eNavigation strategy of International Maritime Organization (IMO), a safe navigation of vessels from berth to berth. The only alternative terrestrial backup for GNSS is the enhanced LOng-RAnge Navigation (eLORAN). However, that system will probably never reach global coverage because a substantial part of LORAN stations was shutdown in the years 2010 to 2015. There are only certain regions where it is still usable as well as Chayka, the Russian system. Today, in case of a temporal loss of GNSS no system can provide absolute positioning in areas with increased necessity for reliable navigation data provision on a global scale. One candidate for a terrestrial backup system is R(anging)-Mode which utilizes existing maritime radio infrastructure for the broadcast of modified signals, which allow distance estimation to the transmitter. First feasibility studies were done in the North Sea, in China and in South Korea. The Baltic Sea is due to the high number of vessels along the shipping routes and its sensitive marine ecosystem a suitable region for the implementation and testing of R-Mode firstly as a terrestrial backup system to GNSS. It will supplement navigation information in order to reduce the risk of collisions and groundings. New marine applications that require a continuous provision of position information will be made possible at sea, in coastal areas and in harbours. A consortium of 12 research institutions, administrations and industry of Germany, Poland, Sweden and Norway is currently engaged to setup the first R-Mode testbed, which utilizes IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities) beacons and Automatic Identification System (AIS) land stations for the broadcast of R-Mode enabled signals. This will be organized in the southern Part of the Baltic Sea until 2020 funded by EU within the scope of R-Mode Baltic Project. Beside this main goal of the Project (back-up to GNSS) further research and development activities will be conducted. This comprises the selection of most suitable way of R-Mode service implementation on IALA beacon and AIS signals without disruption of the legacy service. Further the selection of best algorithms for positioning either with only one type of R-Mode signals or with both is part of the research activities. In addition a concept development and test installation for the time synchronization of a transnational network of R-Mode stations which is currently maintained by independent national maritime administrations as well as the development of first prototypes of R-Mode receiver capable to cope with modified IALA beacons and/or AIS signals as well as R-Mode base station hardware for R-Mode signal generation is planned. Finally, with R-Mode enabled PNT data processing and Pilot Plug Unit the first two R-Mode applications will be made available. All this activities will be done in close contact with the maritime administrations, IALA, and maritime users. It is anticipated, that R-Mode will be a terrestrial backup system that can fulfil their need for robust and reliable PNT data for safe navigation at sea. Beside a general description of the project first results of a common coverage prediction for the IALA beacon and AIS communication channels for the Baltic Sea region will be estimated and presented. Latter shows that especially the relevant Baltic Sea region with its narrow passages of coastlines in the North (Denmark, Norway and Sweden) and in the South (Germany, Poland) is already well equipped with a dense network of IALA beacons and AIS stations in the vicinity of vessel routes. In case of positive Project results, upgrading all of them to R-Mode service level these would allow positioning in the entire region. Furthermore, the output of the requirement phase study on the R-Mode system and its components is presented. This was done starting from relevant documents of IMO and IALA requirements for a terrestrial backup system were extracted and next further broken down for specific components of the R-Mode system. This shall give guidance throughout the project when selecting appropriate algorithms, methods and technical solutions.

This paper addresses the problem of error correction in a multi-user trellis coded system in the presence of bit stuffing. In particular, one considers the situation in which automatic identification system (AIS) signals are received by a satellite. The proposed receiver uses a cyclic redundancy check (CRC) for error correction. A Viterbi algorithm based on a so-called extended trellis is developed. This trellis is defined by extended states composed of a trellis-code state and a CRC state. Moreover, special conditional transitions are defined in order to take into account the possible presence of bit stuffing. The proposed algorithm was first developed in a single-user context. It is generalized in this paper to a multi-user scenario by designing an interference mitigation method. This method allows one to derive a demodulation algorithm whose complexity is almost identical to that obtained in the single-user context. Some performance results are presented in the context of AIS and compared with results provided by existing techniques.

We study the whole process of using Low Earth Orbit (LEO) satellite constellation to localize Automatic Identification System (AIS) emitter, especially the methodologies of intelligently improving and modifying LEO satellite constellation positioning algorithm based on AIS data. In this vein, we design a simulation system for cooperative localization of vessels based on the AIS signals they emit, with a LEO satellite constellation. We implement key modules such as vessel motion simulation, AIS signal simulation, satellite-to-ground link simulation, localization algorithm module and system visualization. The simulation results of single satellite and multi-satellites localization systems verify the effectiveness of the proposed system.

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

An atmospheric refractivity inversion method based on deep learning

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

The vessel monitoring system at the sea which mainly uses Automatic Identification System (AIS) technology will be more effective if it is combined with the satellite technology. This id due to the satellite can cover reach thousands of kilometers when orbiting in the space. Unfortunately, the process of receiving AIS signals from the transmitter has the problem of very low received power that reaches up to −102.07dBm. Therefore the simulation design of CubeSat AIS receiver is required for analyzing received signal prior the deployment. This paper present the signal analysis of CubeSat AIS receiver constructed based on Gaussian Minimum Shift Keying (GMSK) modulation. The AIS receiver as a payload of CubeSat is intended to operate at frequency of 162.025 MHz. It is designed to receive AIS signals in the orbit at an altitude of 650km. The GMSK demodulation of AIS receiver is simulated using an electronic circuit simulation software. The simulation result shows that the CubeSat AIS receiver can receive AIS signals under appropriate conditions satisfying link budget calculation.

The growth of applications relying on the Automatic Identification System (AIS) brings to light the need of verification and trustworthiness of AIS data. AIS data are vulnerable to false or missing declarations requiring the implementation of measures increasing the trustworthiness of AIS messages. One of the possible solution is the comparison between the reported vessel position and the position estimated using for example radiolocalization techniques. The usage of AIS signals for radi-olocalization has been already demonstrated, however the main limitation of this technique is the limited coverage. In order to extend the coverage area, data from different networks should be used. In this case an inter-network time bias has to be considered. This offset is due to the lack of standardization about the specific synchronization technique, hence each AIS receiver manufacturer can implement time extraction using different algorithms. If devices from the same manufacturer are employed, the offset between the receivers is negligible, whereas if dissimilar receivers are used, the offsets could prevent the correct outcome of the classical radiolocalization techniques. In order to fill in this gap, a “Multi-Network (MN) Time Difference Of Arrival (TDOA)” algorithm has been designed. The approach extends the traditional TDOA technique, by working out any synchronization problem. The algorithm has been tested in simulated scenarios. From the results, the effects of the inter-network bias have been investigated and the benefits of the inclusion of heterogeneous stations, either non-synchronized or with different accuracies, have been shown. Moreover, the maximum tolerable anachronism between the receivers included in the network has been evaluated.

Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper describes a simple yet effective niching algorithm, a particle swarm optimization (PSO) algorithm using a ring neighborhood topology, which does not require any niching parameters. A PSO algorithm using the ring topology can operate as a niching algorithm by using individual particles' <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local memories</i> to form a stable network retaining the best positions found so far, while these particles explore the search space more broadly. Given a reasonably large population uniformly distributed in the search space, PSO algorithms using the ring topology are able to form stable niches across different local neighborhoods, eventually locating multiple global/local optima. The complexity of these niching algorithms is only <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> ), where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> is the population size. Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require niching parameters.

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the Automatic Identification System, which serves as a communication medium between neighboring ships and the control center. While traditional methods have relied on sequential tracking and physics-based models, the emergence of deep learning has significantly transformed techniques typically used in trajectory prediction, clustering, and anomaly detection. This transformation is largely attributed to the deep learning algorithm’s capability to model complex nonlinear relationships while capturing both the spatial and temporal dynamics of ship movement. Capitalizing on this computational advantage, our study focuses on evaluating different deep learning architectures such as Multi Model Long Short-Term Memory (LSTM), 1D Convolutional-LSTM, and Temporal-Graph Convolutional Neural Networks— in addressing the problem of track association. The performance of these proposed models are compared against different deep learning algorithms specialized in track association tasks using several real-life AIS datasets.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.