Data-driven methods open up unprecedented possibilities for maritime surveillance using automatic identification system (AIS) data. In this work, we explore deep learning strategies using historical ...AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder–decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines long short-term memory RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority (DMA) show the effectiveness of deep learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the multilayer perceptron architecture. The comparative evaluation of results shows: first, the superiority of attention pooling over static pooling for the specific application, and second, the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.
To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively ...produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62 and -13.77% for container ships, between +2.28 and -3.32% for dry bulk, between -0.22 and -9.27% for wet bulk, and between -19.57 and -42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.
We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation ...and to a time scaling law for the related uncertainty that in the long term is shown to be orders of magnitude lower than under the nearly constant velocity (NCV) assumption. In support of the proposed model, an analysis of a significant portion of real-world maritime traffic is provided.
A novel anomaly detection procedure based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, ...changing its nominal velocity v 0 . In order to hide this behavior, the vessel switches off its automatic identification system (AIS) device for a time T and then tries to revert to the previous nominal velocity v 0 . The decision that has to be made is declaring that a deviation either happened or not, relying only upon two consecutive AIS contacts. Furthermore, the extension to the scenario in which multiple contacts (e.g., radar) are available during the time period T is also considered. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter v 0 of the vessel is the core of the proposed approach and enables the solution of the anomaly detection problem. Closed analytical forms are provided for the detection and false alarm probabilities of the hypothesis test.
Today, the maritime domain is at the cusp of a new era, driven by technological advances in automation, robotics, multisensor perception, and artificial intelligence (AI), together with ...digitalization and connectivity. Smart ship infrastructure and technology, remotely controlled and autonomous ship operation to improve safety, security, cost efficiency, and sustainability are the future of maritime transportation <xref ref-type="bibr" rid="ref1">1 , representing now the engine of 90% of global trade <xref ref-type="bibr" rid="ref2">2 . Ships will soon benefit from recent developments in sensors, telecommunications, and computing technologies to turn the smart shipping revolution into reality <xref ref-type="bibr" rid="ref3">3 and <xref ref-type="bibr" rid="ref4">4 , as it has already happened for autonomous vehicles such as driverless cars, aerial drones, unmanned (or remotely piloted) aircraft, and underwater vehicles.
This article presents a Bayesian approach for sequential detection of anomalies in the motion of a target and joint tracking. The anomaly is modeled as a binary (on/off) switching unknown control ...input that goes into action (begins to exist, or “switches on”) thereby modifying the object dynamics; and by ceasing its activity (becoming nonexistent, or “switching off”) returns the dynamics to nominal. The developed Bayesian framework brings together random finite set (RFS) theory to represent the switching nature of the anomaly, and optimal joint input and state estimation to sequentially update a hybrid state that incorporates a random vector for the kinematic state and a Bernoulli RFS for the unknown control input. In addition, a closed-form solution, the Gaussian-mixture hybrid Bernoulli filter (GM-HBF), has been developed to provide a customized solution for dynamic anomaly detection in the maritime domain characterized by linear Gaussian target dynamics. Based on the Ornstein–Uhlenbeck dynamic model for vessels, where the evolution of the object velocity is governed by a piecewise mean-reverting stochastic process, the anomaly can be represented by a change in the long-run mean velocity (i.e., the unknown control input) from the nominal condition that forces the vessel to deviate from its standard route. We demonstrate the effectiveness of the proposed GM-HBF in both simulated and real-world maritime surveillance applications and test its performance in the face of false measurements, detection uncertainty, and sensor data gaps.
We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed ...representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein-Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system.
During the course of an epidemic, one of the most challenging tasks for authorities is to decide what kind of restrictive measures to introduce and when these should be enforced. In order to take ...informed decisions in a fully rational manner, the onset of a critical regime, characterized by an exponential growth of the contagion, must be identified as quickly as possible. Providing rigorous quantitative tools to detect such an onset represents an important contribution from the scientific community to proactively support the political decision makers. In this paper, leveraging the quickest detection theory, we propose a mathematical model of the COVID-19 pandemic evolution and develop decision tools to rapidly detect the passage from a controlled regime to a critical one. A new sequential test-referred to as MAST (mean-agnostic sequential test)-is presented, and demonstrated on publicly available COVID-19 infection data from different countries. Then, the performance of MAST is investigated for the second pandemic wave, showing an effective trade-off between average decision delay Formula: see text and risk Formula: see text, where Formula: see text is inversely proportional to the time required to declare the need to take unnecessary restrictive measures. To quantify risk, in this paper we adopt as its proxy the average occurrence rate of false alarms, in that a false alarm risks unnecessary social and economic disruption. Ideally, the decision mechanism should react as quick as possible for a given level of risk. We find that all the countries share the same behaviour in terms of quickest detection, specifically the risk scales exponentially with the delay, Formula: see text, where Formula: see text depends on the specific nation. For a reasonably small risk level, say, one possibility in ten thousand (i.e., unmotivated implementation of countermeasures every 27 years, on the average), the proposed algorithm detects the onset of the critical regime with delay between a few days to 3 weeks, much earlier than when the exponential growth becomes evident. Strictly from the quickest-detection perspective adopted in this paper, it turns out that countermeasures against the second epidemic wave have not always been taken in a timely manner. The developed tool can be used to support decisions at different geographic scales (regions, cities, local areas, etc.), levels of risk, instantiations of controlled/critical regime, and is general enough to be applied to different pandemic time-series. Additional analysis and applications of MAST are made available on a dedicated website.
Ship traffic monitoring is a foundation for many maritime security domains, and monitoring system specifications underscore the necessity to track vessels beyond territorial waters. However, vessels ...in open seas are seldom continuously observed. Thus, the problem of long-term vessel prediction becomes crucial. This paper focuses attention on the performance assessment of the Ornstein-Uhlenbeck (OU) model for long-term vessel prediction, compared with usual and well-established nearly constant velocity (NCV) model. Heterogeneous data, such as automatic identification system (AIS) data, high-frequency surface wave radar data, and synthetic aperture radar data, are exploited to this aim. Two different association procedures are also presented to cue dwells in case of gaps in the transmission of AIS messages. Suitable metrics have been introduced for the assessment. Considerable advantages of the OU model are pointed out with respect to the NCV model.
In principle, the Automatic Identification System (AIS) makes covert rendezvous at sea, such as smuggling and piracy, impossible; in practice, AIS can be spoofed or simply disabled. Previous work ...showed a means whereby such deviations can be spotted. Here we play the opponent's side, and describe the least-detectable trajectory that the elusive vessel can take. The opponent's route planning problem is formalized as a non-convex optimization problem capitalizing the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. The velocity of the vessel is modeled with an Ornstein-Uhlenbeck (OU) mean reverting stochastic process, and physical and practical requirements are accounted for by enforcing several constraints at the optimization design stage. To handle the resulting non-convex optimization problem, we propose a globally-optimal and computationally-efficient technique, called the Non-Convex Optimized Stealth Trajectory (N-COST) algorithm. The N-COST algorithm consists amounts to solving multiple convex problems, with the number proportional to the number of segments of the piecewise OU trajectory. The effectiveness of the proposed approach is demonstrated through case studies and a real-world example.
PDF
