Research on AIS Signal Frequency Statistics based on SpringBoot+Vue

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.

In order to verify the feasibility of atmospheric duct inversion method using automatic identification system signal in the actual sea area, a sea experiment was carried out. The signal acquisition system of portable high sensitivity civil automatic identification system is developed, and the receiving and processing process of automatic identification system signal is analysed. The sea experiment scheme and main experiment equipment are given, and the collected experiment data are processed and analysed. Finally, the method of atmospheric duct inversion using automatic identification system signal is verified by the measured data. Atmospheric duct inversion is an inverse problem, which requires the use of various optimisation algorithms to optimise the duct parameters in order to obtain the characteristic parameter information of the duct. In this paper, the Lévy flight quantum‐behaved particle swarm optimisation algorithm and the deep learning algorithm are respectively used to invert the atmospheric duct parameters. The results show that there is little difference between the inverted duct parameters and the true duct parameters, which verifies the feasibility of using the automatic identification system signal to invert the atmospheric duct.

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.

The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.

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.

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 space-based automatic identification system (AIS) has attracted much attention because the system can receive AIS signals from ships on the high seas. As its serious problem, multiple AIS packets can collide at one satellite because it has a wide coverage area. Parallel interference cancellation (PIC) based on Viterbi algorithm (VA) is very effective in separating the collided packets and shows an excellent bit error rate (BER) performance under the condition that Doppler frequencies, channel impulse responses, and propagation delay time are ideally estimated. When the estimation of those channel parameters is not sufficiently accurate with desired to undesired signal power ratio (DUR) being low, however, the BER of PIC can be seriously degraded. In this paper, we propose a novel iterative channel estimation scheme for each channel parameter to improve the estimation accuracy even under the low DUR condition. The least squares and quasi-Newton methods are applied to the estimation of the channel impulse responses and Doppler frequencies, respectively, besides coarse estimation using correlators. The correlation-based estimation of the propagation delay time is repeated. Computer simulations demonstrate that the proposed scheme can estimate the channel parameters more accurately, which leads to remarkable improvement in the BER performance.

The rapid growth of maritime transportation and its regularization have imposed an increasing demand for developing a global maritime surveillance capacity. The existing cooperative maritime traffic monitoring relies on the shipborne automatic identification system (AIS), which is now confronting severe security threats due to the vulnerabilities of AIS at both the implementation and protocol levels. In this article, the possibility of passive AIS signal localization using low Earth orbit satellite constellations is investigated, which provides a more reliable vessel position determination. To this end, the article first presents an overview on two categories of passive localization methods. Realizations of these methods on the satellite-based AIS emitter position determination are then demonstrated and compared. To evaluate the system performance, the lower bound for the localization error is also derived. Focusing on the system realization, some general design considerations and localization accuracy optimization are further discussed.

Radars are already used for collision avoidance during more than 50 years. During this period many proposal for collision avoidance by using radar have been made, and the experience of their use have been gained. However the effectiveness of these proposals for collision avoidance is not enough, especially in congested water. The Automatic Identification System (AIS) will be equipped all ships of 300 gross tonnage and upwards engaged on international voyages. The AIS is expected to be very useful for collision avoidance. Even if AIS is used, it is necessary to consider the effect of predicted error of ship's position on the evaluation of collision risk. In this paper an approach to improve the evaluation and expression of collision risk is shown. This method is able to show the obstacle zone by target (OZT), considered an estimated error of ship's speed, and it will be useful for collision avoidance in congested water. Expression of OZT shown in this paper will be easy to trace the origin target of OZT and to check the fault information transmitted by the target.

Purpose: This study aims to assess the effectiveness of Principal Component Analysis (PCA) in identifying anomalies and improving data quality in AIS (Automatic Identification System) datasets used for maritime monitoring and decision-making. Need for the study: AIS plays a crucial role in intelligent vessel traffic management across the shipping industry. However, despite its widespread adoption, the technology still suffers from issues of data consistency, accuracy, and integrity. These limitations reduce its effectiveness in supporting AI-based decision-making systems. Addressing these shortcomings is essential for improving operational efficiency, safety, and sustainability in maritime transport. Methodology: The research utilizes PCA on a large-scale AIS dataset containing over 800 million records from the Baltic and North Seas. The data underwent filtering and cleaning before analysis to detect patterns, identify anomalies, and evaluate the consistency of variables such as latitude, longitude, draught, and speed. Additionally, t-SNE (t-distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) were explored as complementary nonlinear dimensionality reduction techniques. Findings: PCA proved effective in detecting both extreme and subtle anomalies in the AIS data, including implausible draught values, unusual routing behaviors, and spatial inconsistencies. The analysis demonstrated that even mature systems like AIS can yield unreliable data, emphasizing the necessity for rigorous quality control. Furthermore, the study found that nonlinear methods like t-SNE and UMAP hold promise for enhancing dynamic monitoring. Practical Implications: The integration of PCA-based analytics into maritime monitoring systems enhances early anomaly detection, reduces the risks of decision-making based on faulty data, and improves situational awareness. The study advocates for stronger AIS data governance standards and lays the foundation for future use of real-time anomaly detection and AI-driven decision support in maritime operations.

Automatic Identification System (AIS) allows the ships to broadcast important kinematic and static information, and is one of the most commonly used tools for ship traffic monitoring as individual tracks can be inferred from chronological sequence of the ships' AIS messages. In this paper, we propose two deep learning based methods for AIS data association. The first method predicts a ship's position at the time of a new message and then computes the association probability. The second method computes the association probability directly without ship position interpolation. Both methods use only three AIS data attributes for inference: longitude, latitude and time. We validate the proposed methods' performance with real AIS dataset and show that they achieve reliable data association.

Ship motions are usually employed to analyze water transportation system. Interpolation of ship motions can be used for estimating the lost ship movement information, which is important for analyzing water traffic. Although some methods are proposed to interpolate ship trajectory, these methods are needed to improve interpolation accuracy. In addition, the course and speed of ship are not interpolated in previous studies. This article proposes a novel estimation algorithm for interpolating ship motions. First, the bilateral filtering is used to smooth the navigation trajectories, then a novel interpolation approach forecasts the vessel positions during the interpolation time period by taking into account the dynamic information of Automatic Identification System (AIS), and then forms the interpolation track of the moving vessel. In order to validate the proposed method, the AIS data of Yangtze River is collected from the AIS stations and the proposed restoration methods are verified by MATLAB under simulation. The results show that the proposed method can effectively remove the inaccurate ship motion data and interpolate the lost ship motion data.