A novel estimation algorithm for interpolating ship motion

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

The Automatic identification system is a system used globally by ships that allows the easy identification of other surface contacts (also equipped with an automatic identification system equipment). The main objective of the present work is to validate experimentally the prediction of a ship's movement. A Kalman Filter algorithm, a recursive technique widely used in several scientific areas of study including navigation, is designed to estimate and predict the movement. In this work, a non-linear kinematic model is proposed to analyse and predict the movement of an automatic identification system contact, based on the data transmitted by it. The results evidence that the automatic identification system data can be used as input of a Kalman filter to get consistent and reliable information, an advantage to decision makers so they can act with adequate knowledge and in time.

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.

Artificial Force Fields for Multi-agent Simulations of Maritime Traffic: A Case Study of Chinese Waterway

To let the Tsunami go past safely, the navigators normally navigate ships to the deep-water area that is sometimes said more than 200 meters. In this time, understanding the traffic condition around the ship is very important especially heavy traffic areas for avoiding the collision. It is also true that in such time, the ship's own ship's maneuverability might be hugely decreased because of sea conditions such as Tsunami and the strong current caused by the ebb tide before the Tsunami. These incidents also increase the collision risk. For reducing the risk of collision in such time, it is useful to know the marine traffic condition previously. Then, the navigator estimates the high-risk areas considering the maneuverability. For this purpose, the ship owners demand the marine traffic analysis. Even though the marine traffic observation and analysis have been carried out in various reasons, there are few established ways for analyzing the traffic condition for the emergency situation. For this reason, the author carried out the marine traffic condition analysis by using gas model based on method and the model based on the Obstacle Zone by Target (OZT). The author introduced the model based on a gas molecular collision calculation model for calculating area risk. By applying this method, it is possible to analyze the area in terms of ships' relative angle, size, speed and density. The OZT method is developed to show the high collision risk area on the target ship's course. This method has been studied for avoiding the collision for the own ship in real time situation. In this paper, the author proposed the new method for the marine traffic analysis by using the OZT model. By using this method, it is possible to know the areas that are estimated as collision areas. For one week, Automatic Identification System (AIS) data was collected and analyzed on the Suruga Bay. Finally, the results by using two methods are studied.

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

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.

Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47–51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49–85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5–24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

Semi-supervised learning (SSL) has gained significant attention in the domain of sea state estimation (SSE) due to its capacity to alleviate the reliance of deep learning models on extensive labeled datasets. While existing semi-supervised SSE methodologies leveraging pseudo-labeling have achieved promising results, they often overlook the challenges posed by high class imbalance and the prevalence of missing data in ship motion datasets, which restricts their broader applicability. In this article, we propose a novel SSL approach BalanceSSE based on the class-imbalanced ship motion data for SSE. This approach consists of three main modules: 1) the dynamic imputation (DIT); 2) the imbalance temporal-frequency learning (ITFL); and 3) the ClusterProx classifier (CL). The DIT module dynamically imputes incomplete ship motion data by assigning different weights to various dimensions data. The ITFL module employs time-frequency collaborative learning to generate pseudo-labels and integrate an adaptive confidence strategy to select high confidence pseudo-labels. This process is further enhanced by the CL module to produce better estimates. Experimental tests on UCR datasets and ship motion datasets demonstrate that BalanceSSE outperforms state-of-the-art methods. Ablation studies highlight the critical role of each module in BalanceSSE.

The ship motions in waves are regarded as a nonstationary random process because of the ship maneuvers, such as changes of the course and the speed, even on the assumption that the seaway can be considered as stationary stochastic process. In this paper, the recursive type of Bayesian modeling procedure is proposed and applied to online estimation of directional wave spectra based on ship motion data, which are pitch angle, roll angle and vertical acceleration. In the proposed method, the convergence of the estimates is not achieved every time step, but gradually achieved with time steps by using the ship motion cross spectra. To cope with the nonstationarity included in the time history data, instantaneous cross spectrum analysis based on T-VVAR (Time Varying Coefficient Vector Auto Regressive) modeling procedure is adopted. The T-VVAR model is transformed into the state space model and the time varying coefficients can be evaluated by using the Kalman Filter algorithm. Using the estimated time varying coefficients, the instantaneous ship motion cross spectra can be calculated every moment. Moreover, in order to estimate the directional wave spectra stably, a new constraint condition, which is based on the observed significant wave height data, is introduced into the Bayesian modeling procedure. To examine the reliability of the proposed procedure, onboard tests were carried out on nonstationary conditions. It is shown that the statistical values derived by integration of the estimated directional wave spectra agree well with the observed one.

Notorious for its ravaging climate in winter, the North Pacific is feared among seafarers as theldevil's water. In the past 10 years, it ran havoc with many large vessels and took many lives and much property.So, the Japanese Ministry of Transport has conducted a research project for five years since 1982 to clarify the effect of wave force on hull and develop Computer Aided Navigation System for Safety (CAN). This is 'a kind of shipboard instrumentation system for the sensing of incident waves, ship motions and structural responses and prediction of response changes to be expected when ship's course or speed is altered.This system was installed on a container ship “Japan Alliance” operating in the North Pacific service and checked its functions and performance. During the experiment, from 1986. 11 to 1987. 1, significant full-scale data on ship motions, vertical accelerations, bending moments etc. were obtained. Report here is a presentation of the experimental results on the Computer Aided Navigation System for Safety.

Very short-term ship motion forecasting aims to predict future movements using historical ship motion data. While ship motion features both temporal and frequency characteristics, the latter is often neglected. This paper proposes a fully adaptive time–frequency coupling forecasting model using self-attention mechanism based on the Deep Operator Network (DeepONet), abbreviated as TFD. The multi-head attention layers enable the trunk net to adaptively learn the relationships between different frequencies in the frequency domain and assign varying weights accordingly. Thus, compared to the Fourier transform and multilayer perceptron-net enhance model based on DeepONet (FMD), which relies on manually specified filter frequencies, the TFD model is capable of fully learning the motion patterns in both the time and frequency domains, establishing nonlinear mapping relationships between them. It exhibits greater interpretability and generalization. The TFD model is tested for accuracy and generalization using ship motion data from the Iowa University experimental tank. The results indicate that, compared to the DeepONet and FMD, the TFD model reduces the mean square error (MSE) by up to 64.72% and 52.45%, with an average reduction of 55.57% and 42.47%. In terms of generalization, the forecasting MSE is reduced by up to 65.04% and 46.08%. Compared to the DeepONet and FMD, the proposed TFD model demonstrates significant improvements in forecasting horizon and generalization, providing a notable advantage in very short-term ship motion prediction applications.

Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm (GA), back propagation neural network (BPNN) prediction model and SVR prediction model are respectively established, and the final prediction results are obtained by results of three models. And the weights coefficients are determined by the correlation coefficients, reducing the risk of prediction and improving the reliability. The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more reliable. The mean absolute percentage error (MAPE) of the proposed model is 0.84%, but the results of the single models are greater than 1%.

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities

선박교통류 평가는 대상해역 해상교통특성 파악을 통하여 정량화된 체계화된 교통관리를 수행함으로써 해양사고를 예방하는데 중요하다. 해상교통류의 특성은 선박자동식별장치(AIS, Automatic Identification System)가 선박에 설치됨에 따라 이 장치에서 수신된 정보를 토대로 평가할 수 있다. 본 연구에서는 해상교통관제센터(VTS, Vessel Traffic Services)에서 선박으로부터 수집된 위치, 속력, 침로, 시간 정보로부터 항로상에 해상교통류를 평가하기 위한 시뮬레이터를 구현하였다. 그 결과 해상교통관제센터에서 수집된 데이터로부터 선박 통항량, 통항분포, 선박속력분포, 기하학적 충돌빈도가 계산되어 표시된다. 이러한 해상교통류평가 평가는 해상교통관제사가 해상교통관리의 관점에서 해상안전을 위한 서비스를 제공하는데 유효하게 활용될 수 있을 것으로 사료된다. Maritime traffic assessment is important to understand the characteristics of maritime traffic and to prevent maritime accidents. The maritime traffic assessment can be calculated from the ship trajectory data observed by using AIS(Automatic Identification System). This paper developes a maritime traffic assessment tool using ship's position and speed, course, time data from ships navigating waterways. The results are represented in terms of the number of traffic quantity and traffic distribution, speed distribution, geometric collision candidates. The developed tool will contributes to advance maritime traffic safety by VTS(Vessel Traffic Services).