A novel ship trajectory reconstruction approach based on low-rank tensor completion

In response to the presence of noise and missing data in the records of ship trajectories from the Automatic Identification System (AIS), which affects the accuracy of ship traffic flow modeling and knowledge discovery, an adaptive reconstruction method for ship trajectories is proposed, taking into account the state of motion of the ship. This method comprehensively considers latitude, longitude, speed, course, and turn rate in the ship trajectory data and enables the identification and removal of position, speed, and course noise. In addition, the ship’s trajectory is divided into straight segments and turning segments based on the rate of change of the ship’s course. Various interpolation methods are adaptively applied to repair the trajectory. The validation with real ship trajectories shows that the proposed method has higher accuracy than other benchmark methods and provides a solid basis for evaluating ship trajectories.

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

Vessel trajectory information plays a critical role in vessel traffic management systems by enhancing navigational safety. However, trajectory reconstruction remains challenging due to the inherent noise and incompleteness in automatic identification system (AIS) measurements. In this paper, we propose non-homogeneous Markov chain driven interacting multiple model (NHMC-IMM), which is a novel adaptive information fusion approach by integrating multiple motion models to adapt to the diversity of target motion. The gradient descent algorithm is applied to the improved IMM, which is used to adjust the covariance matrix of the process noise and the measurement noise during the execution of the IMM. In this way, the accuracy of the model estimation is improved by updating the parameters continuously and iteratively under the given conditions of the observed measurement data, and the optimal parameters will be selected at the end of the iteration, thus enhancing the accuracy of trajectory reconstruction. To validate the effectiveness of the proposed method, experiments were conducted using AIS datasets from the coastal regions of the United States and Denmark, under varying levels of local measurement missingness. Results demonstrate superior accuracy and robustness in trajectory reconstruction, highlighting the method’s potential for improving maritime measurement systems and enhancing situational awareness.

Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.

A maritime traffic route extraction method based on density-based spatial clustering of applications with noise for multi-dimensional data

A trajectory is a sequence of observations in time and space, for examples, the path formed by maritime vessels, orbital debris, or aircraft. It is important to track and reconstruct vessel trajectories using the Automated Identification System (AIS) data in real-world applications for maritime navigation safety. In this project, we use the National Science Foundation (NSF)'s Algorithms for Threat Detection program (ATD) 2019 Challenge AIS data to develop novel trajectory reconstruction method. Given a sequence of N unlabeled timestamped observations Χ={x1,x2,...,xN}, the goal is to track trajectories by clustering the AIS points with predicted positions using the information from the true trajectories Χ. It is a natural way to connect the observed point xî with the closest point that is estimated by using the location, time, speed, and angle information from a set of the points under consideration xi ∀ i ∈ {1, 2, …, N}. The introduced method is an unsupervised clustering-based method that does not train a supervised model which may incur a significant computational cost, so it leads to a real-time, reliable, and accurate trajectory reconstruction method. Our experimental results show that the proposed method successfully clusters vessel trajectories.

A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters.

High-quality ship trajectory clustering is an important fundament for learning meaningful knowledge from massive historical trajectories, which are commonly extracted from the shipborne Automatic Identification System (AIS) data. However, clustering of massive trajectory data easily suffers from two limitations, i.e., low accuracy and high computational cost. In this work, we propose to develop an efficient ship trajectory clustering method based on the virtual maritime traffic network, which is constructed using the massive AIS data. In particular, we first adopt the popular Douglas-Peucker (DP) algorithm to simplify the ship trajectories to obtain the first type of feature points, where the moving ships change the course significantly. The second type of features points, where exist the large number of ship encounters, is also obtained through estimating the ship density map. The density-based clustering method is employed to remove the unwanted outliers in these feature points, contributing to the extraction of important waypoints. The virtual maritime traffic network is then constructed using the extracted way-points. To obviously shorten the computational cost, the virtual network is finally used to cluster the massive ship trajectories by introducing the concepts of base cluster and stream cluster. Two famous metrics, i.e., Silhouette Coefficient (SC) and Davies-Bouldin Index (DBI), will be exploited to evaluate the trajectory clustering results for our method and several competing methods. Experimental results on realistic AIS data have demonstrated the superior clustering performance of our method in terms of computational efficiency, SC and DBI metrics.

In recent years, the prediction of ship trajectory based on automatic identification system (AIS) data has become an important area of research. Among the existing studies, most focus on a single ship to extract features and train models for trajectory prediction. However, in a real situation, AIS contains a variety of ships and trajectories that need a general model to serve various cases. Therefore, in this paper, we include an attentional mechanism to train a multi-trajectory prediction model. There are three major processes in our model. Firstly, we improve the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm and apply it to trajectory clustering. According to the clustering process, ship trajectories can be automatically separated by groups. Secondly, we propose a feature extraction method based on a hierarchical clustering method for a trajectory group. According to the extraction process, typical trajectories can be obtained for individual groups. Thirdly, we propose a multi-trajectory prediction model based on an attentional mechanism. The proposed model was trained using typical trajectories and tested using original trajectories. In the experiments, we chose nearby port waters as the target, which contain various ships and trajectories, to validate our model. The experimental results show that the mean absolute errors (MAEs) of the model in longitude (°) and latitude (°) compared with the baseline methods were reduced by 8.69% and 6.12%.

Global navigation satellite systems (GNSS) play a decisive role in maritime navigation. The differential mode of operation of ship-based GNSS receivers using coordinate corrections allows to significantly increase the accuracy of positioning of a seagoing vessel compared to the autonomous mode. The object of the study was marine GNSS receivers capable of operating in differential mode. This research examines the problems of reliably determining the actual operating mode of a shipboard GNSS receiver (autonomous or differential). It outlines the risks associated with the ambiguity and unreliability of standard differential mode indicators (flags posMode = D, Fix Quality = 2). This leads to misinterpretation of the accuracy status by related navigation systems, in particular, the Automatic Identification System (AIS), and poses a threat to maritime safety. It has been experimentally proven that outdated receiver models can falsely indicate operation in differential mode, relying solely on user settings rather than the actual receipt and application of corrections. It has been established that modern receivers solve this problem but create a new level of complexity by separating the concepts of “accuracy” and “integrity” of the navigation solution. They can produce a highly accurate position while simultaneously flagging it as unreliable (NavStatus flag = V) if a faulty satellite is detected. A systemic conflict between the requirements of the International Telecommunication Union (ITU) and International Electrotechnical Commission (IEC) standards regarding high-accuracy criteria for AIS has been identified. It has been established that inaccurate mode indications in outdated equipment are related to the particularities of its software logic, which links the mode flag to the setting rather than to the availability of data. The behavior of modern receivers is explained by the implementation of advanced integrity control algorithms (RAIM) and the logic of new standards (in particular, IEC 61108-7), which require reporting the loss of confidence in data. The research results can be used by developers of marine equipment (AIS, ECDIS) to create comprehensive GNSS data analysis algorithms that take into account a set of indicators. International organizations (IMO, ITU) can use them to harmonize standards. Ship operators and technical specialists can use these results to form a correct understanding of the limitations of standard indicators and the need for a comprehensive assessment of the status of the GNSS receiver.

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

Magnetic particle tracking (MPT) is a recently developed non-invasive measurement technique that has gained popularity for studying dense particulate or granular flows. This method involves tracking the trajectory of a magnetically labeled particle, the field of which is modeled as a dipole. The nature of this method allows it to be used in opaque environments, which can be highly beneficial for the measurement of dense particle dynamics. However, since the magnetic field of the particle used is weak, the signal-to-noise ratio is usually low. The noise from the measuring devices contaminates the reconstruction of the magnetic tracer's trajectory. A filter is then needed to reduce the noise in the final trajectory results. In this work, we present a neural network-based framework for MPT trajectory reconstruction and filtering, which yields accurate results and operates at very high speed. The reconstruction derived from this framework is compared to the state-of-the-art extended Kalman filter-based reconstruction.

Vessel handling guidelines in Tampa Bay have been developed and refined over many years to prevent vessel groundings, collisions, and other maritime incidents. These guidelines restrict navigation based upon vessel characteristics such as vessel length, draft, displacement, and destination, often in conjunction with tidal levels and current speeds. A numerical circulation model of the Tampa Bay estuary was used to examine the occurrence frequency of the most common limiting current speeds within the main shipping channel under four sea level rise (SLR) scenarios ranging from 0.25 to 2 m. Near the bay mouth, the occurrence of limiting speeds generally decreased with SLR, but farther up the channel it increased, mostly due to changes in the M2 tide, with contributions from other primary tides. The relative occurrence of slack water, the lowest of the limiting speeds, was most sensitive to SLR, with absolute changes in occurrence ~50%. Higher speed limits had smaller relative changes. A methodology is developed using data from the Automatic Identification System that identifies vessel traffic potentially impacted by SLR and changing tidal currents. Some implications of reduced tidal currents for port and vessel traffic management are discussed.

The spoofing of Automatic Identification System (AIS) messages presents a hazard to safe maritime navigation. To prevent such spoofing, we present an authentication system based on Public Key Cryptography (PKC) that is both fully open source and backwards compatible with mariners’ existing use of the AIS. Using this, we have successfully demonstrated the ‘live’, over-the-air broadcast of authenticated AIS messages in a busy radio environment. The technique used is an improvement upon earlier work in that digital signatures are carried using the terrestrial VHF Data Exchange (VDE-TER) component of the VHF Data Exchange System (VDES). This prevents additional channel loading on the AIS and offers greater flexibility.