Ship Automatic Identification System Research Articles

Association of AIS and Radar Data in Intelligent Navigation in Inland Waterways Based on Trajectory Characteristics

Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety.

An approach to detect identity spoofing in AIS messages

Automatic identification system (AIS) is a maritime communication system that uses a transceiver to automatically exchange navigational data. These data help navigation and allow to monitor the maritime traffic. However, this system can be hacked and malicious users can easily transmit false data to mislead the coastguards or vessels navigating around. While previous research has proposed methods to detect these falsifications, none of them suggest strategies that detect AIS identity spoofing combining the tracking of the ship position and AIS transceiver’s carrier frequency offset (CFO). The CFO, caused by the carrier frequencies mismatch between emitter and receiver and Doppler effect, is used as a radiometric signature to identify materially every transceiver independently of its transmitted identity. It can drift over time and this is why it is tracked thanks to a Kalman filter (KF). In addition, position is also considered to reduce the miss probability of spoofing detection. The KF is noise adaptive to be robust against various CFO drifts and noise levels of the environment. The strategy is tested on real AIS data and the results demonstrate its efficacy: false alarm and miss probabilities were respectively 1% and 1.7%. These results show the ability of the test to correctly detect identity spoofing and the interest of CFO as a radiometric signature. This signature, used for the first time in an AIS application, could be used with other signatures in a future work to improve identity spoofing detection. This is why we made open source in GitHub our algorithm and the real AIS data used.

A novel algorithm for ship characteristic points extraction based on density clustering

With the widespread application of ship Automatic Identification System (AIS) in maritime operations, a large number of ship trajectories become available. This study aims to improve the safety of ships navigating through densely trafficked areas and address the challenge of sufficient data exploration while fully describing the traffic conditions in these waters. To achieve this objective, traffic flow information is extracted from AIS data collected in Zhoushan waters. A combination of multi-algorithms is employed to extract the traffic flow frame, specifically, the Douglas-Peucker compression algorithm and trajectory intersection algorithm are utilised to identify the characteristic points of ship trajectories. Subsequently, a density clustering algorithm is applied to extract the three types of characteristic points: compressed trajectory points, intersection points, and ship position points, facilitating data mining efforts. The resulting initial traffic flow characteristic points are then subject to weighted fusion, followed by image superposition processing to create an overlapping map of the ship trajectories. This process culminates in the generation of a traffic flow frame for the region. The framework integrates various track characteristic points, offering insights into the distribution of essential routes in the vicinity waters, thereby providing a comprehensive depiction of ship traffic flow patterns. The proposed framework can be applied to the route planning and serve as a reference to the maritime authorities when selecting recommended shipping lanes.

An approach for traffic pattern recognition integration of ship AIS data and port geospatial features

ABSTRACT Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

Construction of a Real-Time Ship Trajectory Prediction Model Based on Ship Automatic Identification System Data

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

A novel ship trajectory clustering analysis and anomaly detection method based on AIS data

The increasing volume of ship traffic has resulted in new challenges for the supervision of maritime safety administration. The conventional manual monitoring approach for maritime traffic is inefficient and lacks specifics, particularly for supervising ships with abnormal trajectories. To address this issue, this study proposes utilizing the minimum description length criterion to extract features from ship trajectory data provided by the automatic identification system (AIS). This approach simplifies the compression of ship trajectories. Additionally, the dynamic time warping trajectory similarity measurement algorithm is employed to optimize the density-based spatial clustering of applications with noise algorithm. This optimization enables the clustering of ship trajectories and the acquisition of normalized ship motion trajectories. Furthermore, a ship trajectory prediction method based on a transformer model is proposed, and the normalized motion trajectory is used as the training set for model training. The trained ship trajectory prediction model is subsequently utilized to predict the target ship trajectory. The AIS ship trajectory data in the vicinity of Yantai Port were used for experimental verification. The results demonstrate the effectiveness of the proposed approach in identifying abnormal ship trajectories.

Monitoring and evaluation of ship operation congestion status at container ports based on AIS data

With the rapid development of the shipping industry, many factors, such as larger ships, harsh weather, workers' strikes, surging port throughputs, and the global pandemic, have made port congestion frequent worldwide. How to effectively monitor and evaluate the ship operation congestion status at ports becomes a key challenge for global ports to seek sustainable development. Based on Automatic Identification System (AIS) data, this article analyzed the semantics of ship trajectory by using data mining methods such as the sliding window algorithm, explored ship berthing and berth-waiting operation events, and established models for calculating the congestion index at ports, as well as the time cost, capacity resource cost, and economic cost caused by ship delays to monitor and evaluate the ship operation congestion status at ports. The results showed that the monitoring and evaluation system for container ports based on AIS ship data can effectively monitor and evaluate the port congestion status and support the decision-making efforts of all parties on the supply chain, such as ports, ship enterprises, cargo owners, and shipping agents.

Graph Signal Variation Detection: A novel approach for identifying and reconstructing ship AIS tangled trajectories

Tangles are a common anomaly in Automatic Identification System (AIS) data, resulting from disorder in time sequences. They increase the spatial complexity of trajectories, making existing methods unable to fully recognize and process them. This will seriously affect the analysis of ship behavior. Furthermore, existing methods often directly remove after recognition, resulting in a significant loss of local information. To address these challenges, this paper proposes an algorithm called Graph Signal Variation Detection (GSVD) to recognize and reconstruct tangles. Initially, data preprocessing is conducted to remove redundant and stationary points. Then, construct the vertices and edges of the graph using indicators integrating AIS data features and ship trajectories, respectively. The tangled region is identified by calculating the total variation theory of the graph. The tangled region is reconstructed using the greedy heuristic, and the optimal reconstruction path is determined by combining total variation quantization. Finally, the GSVD is compared with four prevailing methods, through qualitative and quantitative analyses. The results demonstrate that the GSVD surpasses the other methods in terms of recognition accuracy, recall rates, and false alarm rates. Additionally, the GSVD can effectively reconstruct the tangle.

Classification of inbound and outbound ships using convolutional neural networks

In general, a single scalar hydrophone cannot determine the orientation of an underwater acoustic target. However, through a study of sea trial experimental data, the authors found that the sound field interference structures of inbound and outbound ships differ owing to changes in the topography of the shallow continental shelf. Based on this difference, four different convolutional neural networks (CNNs), AlexNet, visual geometry group, residual network (ResNet), and dense convolutional network (DenseNet), are trained to classify inbound and outbound ships using only a single scalar hydrophone. Two datasets, a simulation and a sea trial, are used in the CNNs. Each dataset is divided into a training set and a test set according to the proportion of 40% to 60%. The simulation dataset is generated using underwater acoustic propagation software, with surface ships of different parameters (tonnage, speed, draft) modeled as various acoustic sources. The experimental dataset is obtained using submersible buoys placed near Qingdao Port, including 321 target ships. The ships in the dataset are labeled inbound or outbound using ship automatic identification system data. The results showed that the accuracy of the four CNNs based on the sea trial dataset in judging vessels’ inbound and outbound situations is above 90%, among which the accuracy of DenseNet is as high as 99.2%. This study also explains the physical principle of classifying inbound and outbound ships by analyzing the low-frequency analysis and recording diagram of the broadband noise radiated by the ships. This method can monitor ships entering and leaving ports illegally and with abnormal courses in specific sea areas.

Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar

The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%.

Ship Collision Realtime Early Warning System Based on Automatic Identification System (AIS) Data

One of the efforts to improve shipping safety is using the Automatic Identification System (AIS) according to the International Maritime Organization (IMO) recommendations. By utilizing Automatic Identification System (AIS) data, the system can monitor AIS ship movement patterns and compare them with the latest conditions. Information such as the ship's position, destination and speed is tracked. The author will create a real-time ship collision early warning system based on AIS data in this final project. The method used is the haversine formula to calculate the distance between two coordinate points on the earth's surface based on the ship's latitude and longitude. The results of AIS data in the form of latitude, longitude, and ID numbers will be processed using PHP to determine the distance and angle of the ship in real time. From 50 tests on two ships, two conditions can cause ship collisions. The results of these tests are that the percentage of collisions is 4%. The real-time system helps know the ship's actual movement based on current conditions

Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm.

The autonomous generation of routes is an important part of ship intelligence and it can be realized by deep learning of the big data of automatic identification system (AIS) ship trajectories. In this study, to make the routes generated by long short-term memory (LSTM) artificial neural network more accurate and efficient, a ship route autonomous generation scheme is proposed based on AIS ship trajectory big data and improved multi-task LSTM artificial neural network. By introducing an unsupervised trajectory separation mechanism into LSTM, a fast and accurate separation of trajectories with similar paths is realized. In the process of route generation, first of all, a clustering algorithm is used to cluster the trajectories in massive AIS data according to the density of trajectory points, so as to eliminate the trajectories in the routes that do not belong to the target area. Furthermore, the routes are classified according to the type of ships, and then the classified trajectories are processed and used as datasets. Based on these datasets, an improved LSTM algorithm is used to generate ship routes autonomously. The results show the improved LSTM works better than LSTM when the generated route trajectories are short.

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process.

Assessment Method Based on AIS Data Combining the Velocity Obstacle Method and Pareto Selection for the Collision Risk of Inland Ships

A ship collision risk assessment model is an essential part of ship safety navigation. At present, the open water collision risk assessment model (such as the closest point of approach) is applied, but a ship collision risk model suitable for inland rivers is still in the exploration stage. Compared with open waters, the inland waterway has a larger density of ships, and the land and water environments are complex. The existing risk assessment models lack adaptability under the conditions of inland navigation. Therefore, this paper proposes a real-time collision risk assessment method for ships navigating inland rivers. This method utilizes the information of ships’ size in the automatic identification system (AIS) to construct the velocity obstacle cone between convex polygonal targets using the velocity obstacle method. Then, according to the geometric relationship between the relative velocity of two targets and the velocity obstacle cone, a new collision risk assessment model is defined. This model defines two indicators to evaluate the navigation collision risk: the degree of velocity obstacle intrusion (DVOI) and time of velocity obstacle intrusion (TVOI). These two indicators assess the risk of collision, respectively, from two aspects speed and course. In addition, a method using a trajectory compression algorithm to screen collision avoidance operation points in ship AIS trajectory is proposed to screen collision avoidance scenarios in the Yangtze River waterway. The effectiveness of the proposed collision risk model is verified in course-keeping and collision avoidance scenarios and compared with the traditional closest point of approach (CPA) method. The results indicate that the evaluation model for collision risk assessment is more accurate than the CPA method in all scenarios. Finally, this paper uses the Pareto selection algorithm to combine DVOI and TVOI, which can identify the ship that poses the greatest risk to our ship.

Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.

Ship Classification and Anomaly Detection Based on Spaceborne AIS Data Considering Behavior Characteristics.

With the establishment of satellite constellations equipped with ship automatic identification system (AIS) receivers, the amount of AIS data is continuously increasing, and AIS data have become an important part of ocean big data. To further improve the ability to use AIS data for maritime surveillance, it is necessary to explore a ship classification and anomaly detection method suitable for spaceborne AIS data. Therefore, this paper proposes a ship classification and anomaly detection method based on machine learning that considers ship behavior characteristics for spaceborne AIS data. In view of the characteristics of different types of ships, this paper introduces the extraction and analysis of ship behavior characteristics in addition to traditional geometric features and discusses the ability of the proposed method for ship classification and anomaly detection. The experimental results show that the classification accuracy of the five types of ships can reach 92.70%, and the system can achieve better results in the other classification evaluation metrics by considering the ship behavior characteristics. In addition, this method can accurately detect anomalous ships, which further proves the effectiveness and feasibility of the proposed method.

Monitoring Kapal Menggunakan Automatic Identification System(AIS) Dengan RTL-SDR dan Low Noise Amplifier (LNA)

Automatic Identification System (AIS) is a ship transponder that uses MMSI data, speed, position, destination, ship type, and size to locate, track, and monitor ships. Only Vessel Traffic Services (VTS) and a few other agencies can be supervised presently. This is one of the issues that must be resolved. To resolve this issue, hardware that can receive AIS signals at 161.975 MHz and 162.025 MHz and convert them into information signals is required. RTL-SDR is hardware capable of receiving signals in the frequency range of 25-1700 MHz. Its antenna is used to achieve maximum signal reception by establishing a direct line of sight to the AIS data source. Yagi antennas can only receive signals from a single direction, the front. Low Noise Amplifier (LNA) is also used in order to optimize the signal received by the antenna. The signal can be processed and decoded using SDR-Sharp and AISMon to provide data that can be plotted on OpenCPN. The performance of the AIS data decoding process is controlled by the strength and weakness of the signal that the RTL-SDR receiver can receive. Therefore, the antenna and receiver must be placed in a clear line of sight (LOS) with the ship's AIS transponder emitting source. It is envisaged that this monitoring system would make it easier to monitor ships in real-time

The Mathematical Model of Marine Engine Room Equipment Based on Machine Learning

This study is aimed at reducing the occurrence of oil spill accidents in the engine room of ships and carries out risk prevention for the equipment of the port ships, thereby reducing pollution to the marine ecological environment. Firstly, the concepts and principles of cluster analysis and ship automatic identification system are expounded. Secondly, the data information collected by the ship’s automatic identification system (AIS) is combined with density-based cluster analysis. The accident area and extent at different stages of the ship’s engine room equipment are classified. Finally, cluster analysis is used to evaluate the risk of equipment of the port ship engine room. The results show that there are 43000 ship operation information points in port a, the average operating speed of ships is 9 kilometers, and the fastest operation speed is 16.9 kilometers. In addition, many ship routes in port a need to take risk prevention measures to minimize the impact between ships and reduce the risk of oil leakage in the engine room. The proposed cabin model can easily and quickly analyze the orientation information of the ship and classify all the data into different types according to the surrounding information points. AIS can realize the information transfer between ships and between ship and shore. Information such as the position, speed, and direction of the ship needs to be accurately known to ensure safety at sea. These data need to relate to some terminals and networks to form a maritime monitoring network. The ship AIS based on cluster analysis can cluster the areas where the ship’s speed and direction change significantly in the port area, effectively preventing accidents. Scientific risk prevention measures can effectively reduce the oil leakage risk of ship engine room equipment, improve the working efficiency of marine engines, and provide a strong foundation for the entire marine ecological environment protection.

MASS autonomous navigation system based on AIS big data with dueling deep Q networks prioritized replay reinforcement learning

Ship autonomous navigation constitutes the most critical step in intelligent ships and is the major prerequisite for the all task completion of the marine autonomous surface ship (MASS). Reinforcement learning (RL) has been widely used in unmanned transport vehicles because of its excellent performance in solving continuous handling problems. This study proposes a MASS autonomous navigation system using dueling deep Q networks prioritized replay (Dueling-DQNPR) based on the ship automatic identification system (AIS) big data. A navigation environment with three difficulty levels were established to train the Dueling-DQNPR network in sequence by setting a reward mechanism. Moreover, the Dueling-DQNPR was improved by combining the prioritized experience replay, dueling structure and long–short-term memory unit to increase the network depth and ability to process continuous data. Finally, simulation training for the AIS trajectory data was carried out in waters near Zhoushan port. The results demonstrated that, through trial and error, the MASS could be controlled to arrive at its destination without collision. As a result, the proposed method may be applicable to MASS on-duty technology and intelligent ships.

The small world of global marine crude oil trade based on crude oil tanker flows

Global marine crude oil trade is a direct reflection of global economic trends. This study uses the real-world historical ship automatic identification system (AIS) data from 2014 to 2017 to explore the flows of crude oil tanker trade between ports and construct a port-node global crude oil trade model. An analysis framework of “AIS data -> crude oil tanker trade flows -> port-node crude oil trade network -> port-node crude oil trade backbone network” is proposed to realize the progressive analysis of global marine crude oil trade. The global port-node crude oil trade network is a small world, in which there are multiple small world port-node trade communities. Compared with the community structure of the global port-node crude oil trade in previous years, the Middle East–Southern Africa–South Asia–Oceania–Asia Pacific community and the Southern Africa–China community have more activities in port nodes and trade links in 2017, which reflects that the Asia-Pacific region, especially East Asian countries, is making efforts to expand the Oceania and African markets and increase the diversity of crude oil import sources to ensure the security of crude oil supply and demand.