Interference mitigation and error correction method for AIS signals received by satellite

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.

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.

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 proliferation of active radar sensors deployed in vehicles has increased the need for mitigating automotive radar-to-radar interference. While simple avoidance and mitigation methods are still effective today, the expected crowded spectrum allocations pose new challenges that likely require more sophisticated techniques. In particular, interference mitigation methods that can handle significant levels of radar signal corruption are required. To this end, we propose neurally augmented analytically learned fast iterative shrinkage thresholding algorithm (NA-ALFISTA), which is a neural network-based solution for reconstructing time-domain radar signals by leveraging sparsity in the range-Doppler map (RDM). The neural augmentation network is deployed as a single gated recurrent unit (GRU) cell that captures the radar signal statistics along the unfolded layers of fast-iterative shrinkage thresholding algorithm (FISTA)-based sparse recovery, which significantly boosts the convergence rate. It estimates the next layer’s parameters necessary in ALFISTA based on the previous layer’s output. The proposed method is compared to state-of-the-art detect-and-repair methods and source separation methods in simulated data and real-world measurements.

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.

In computer communication and telecommunication applications, error detection and correction techniques must be employed to ensure a reliable data transmission from the source to the destination. Two of the most prevailing techniques, Cyclic Redundancy Check (CRC) detection and Hamming code correction, are implemented and analyzed. The CRC method picks up prominence because it joins three focal points: extraordinary blunder identification capacities, minimal overhead, and simplicity of usage. Moreover, both the CRC and Hamming code are binary linear codes. However, one significant difference is that Hamming code only works on data of some fixed size (depending on the Hamming code used), whereas CRC is a convolution code that works on data of any size. In this paper, the authors will show how CRC helps in removing errors by passing three distorted signals and using CRC to receive the signal error free in the MATLAB environment.

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.

In this paper, a new list decoding algorithm for tail-biting convolutional codes (TBCCs) with a cyclic redundancy check (CRC) is proposed, where the CRC is considered as a concatenated outer code. The main idea of the proposed algorithm is to modify the list decoding procedure of the TBCC by using the CRC. Two algorithms are proposed for the list decoding of the TBCC with the CRC. The first proposed algorithm is a new initial state estimating algorithm using re-encoded CRC bits and having the low computational complexity. The other proposed algorithm is a modified list Viterbi algorithm, where trellis paths are fixed by re-encoded CRC bits and some CRC bits are used for the error correction. For the TBCC concatenated with the CRC code defined in the long-term evolution standard, the proposed decoding scheme by partially using CRC bits outperforms the conventional list decoding algorithms for the list size $L=4$ even though the proposed algorithm has the lower decoding complexity.

Orthogonal frequency division multiplexing (OFDM)-based wireless transmission technologies such as WiMAX are now widely deployed, and the frame check sequence (FCS) scheme is used to enhance the reliability of OFDM-based systems. As the padding overhead cannot be completely avoided in such systems when variable-length medium access control (MAC) frames are encapsulated, the authors propose a novel cyclic redundancy check (CRC)-based error-correction scheme that utilises the frame padding overhead to carry as much extra segmented FCS information as possible. In a multi-hop relay environment, a mobile station might receive a multicast frame more than once, so the error bit correction scheme could be triggered in the first transmission. In the IEEE's work, the original CRC scheme is optional and only has limited error-detection capability in upper MAC layer. FCS requires a few bits in a frame, but in our work, CRC is compulsory and all sub-FCSs are carried by padding space without using any extra bit. For the best error-correction capability, all transmissions must be padded as many sub-FCSs are possible in cross layer. The proposed approach achieves a significant performance improvement over the legacy FCS scheme in terms of error detection and correction of multicast frames in a transparent mode multi-hop relay network, and it is redundancy-free as well as energy efficient. The authors assess the performance improvement via both mathematical analysis and simulations. The results show that the mobile stations get significant improvement of goodput and utilisation in an error-prone WiMAX multi-hop relay network.

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.

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.