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The coronavirus epidemic (COVID-19) is a public health challenge due to its rapid global spread. Its unprecedented speed and pervasiveness have led many governments to implement a series of countermeasures, such as lock-downs, stopping/restricting travels, and mandating social distancing. To control and prevent the spread of COVID-19, it is essential to understand the latent dynamics of the disease’s evolution and the effectiveness of the intervention policies. Hidden Markov models (HMMs) capture both randomnesses in spatio-temporal dynamics and uncertainty in observations. In this paper, we apply an overall HMM that, based on multiple nations’ COVID-19 data including the USA, several European countries, and countries that have strict control policies, explore different types of observations, and we use it to infer the severity state on small geographical states or regions in the USA and Italy as test cases. Further, we aggregate the severity level of each region over a fixed time period to visualize the time evolution and propagation across regions. Such an analysis and visualization provide suggestions for interventions and responses in a calibrated manner. Results from HMM modeling are consistent with what is observed in Italy and the USA and these models can serve as visualization and proactive decision support tools to policymakers.

Recent research activities have investigated the feasibility of detecting ship targets from Global Navigation Satellite System-Reflectometry (GNSS-R) receivers. It was concluded by both experimental and theoretical analyses that ship detection is infeasible from spaceborne receivers working in a conventional configuration. On the other hand, it was demonstrated that performance might be boosted by receiving the right-hand circular polarization (RHCP) channel in a backscattering configuration. In this paper, we assess the detectability of ship targets in GNSS-R delay-Doppler maps by means of a reliable stochastic GNSS-R simulation tool capable of accounting for the presence of the target. The results mainly confirm the previous theoretical analyses and demonstrate the feasibility of the ship detection problem using low-altitudes RHCP-backscattering GNSS-R.

In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determine the behaviour of typical trajectories of the model. Estimation of these parameters from trajectory samples facilitates learning of patterns and training of predictive models using trajectory data, e.g., automatic identification system (AIS) messages transmitted by vessels. We propose a six-degrees-of-freedom parameterisation and investigate the identifiability of these parameters using the Cramer-Rao bound matrix which we estimate using Monte Carlo methods. We demonstrate that some parameter configurations of interest are identifiable and their maximum likelihood estimate can be found using iterative optimisation algorithms. We demonstrate the efficacy of this approach on both simulated and real data.

Piecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent an anomaly as any deviation of the long-run mean velocity from the nominal condition. This amounts to modeling the anomaly as an unknown switching control input that can affect the dynamics of the object. Under this model, the problem of joint anomaly detection and tracking can be addressed within the Bayesian random set framework by means of a hybrid Bernoulli filter (HBF) that sequentially estimates a Bernoulli random set (empty under nominal behavior) for the unknown long-run mean velocity, and a random vector for the kinematic state of the object. An additional challenge is represented by the fact that two extra parameters, i.e. the reversion rate and the noise covariance of the underlying OU process, need to be specified for Bayes-optimal prediction. We propose a multiple-model adaptive filter (MMA-HBF) for anomaly detection, tracking and simultaneous estimation of the OU unknown parameters. The effectiveness of these tools is demonstrated on a simulated maritime scenario.

A new methodology is proposed to deceive an anomalous trajectory detector by designing ship paths that deviate from the nominal traffic routes in an optimized way. The route planning is formalized as a min-max problem (with respect to surveillance system acquisition instants) focusing on the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. Modeling the vessel’s dynamic according to the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process, physical, practical, and kinematic requirements are also accounted for forcing several constraints at the design stage. A computationally efficient technique is proposed to handle the resulting non-convex optimization problem, and some case studies are reported to assess its effectiveness.

We present a hypothesis test to associate ship track measurements to an edge of a given graph that statistically models common traffic routes in a given area of interest. The association algorithm is based on the hypothesis that ship velocities are modeled by mean-reverting stochastic processes. Prior knowledge about the traffic is provided by the graph in form of probability density functions of the mean-reverting kinematic parameters for each node and edge of the graph, that are exploited in the formalization of the association algorithm. Tests on real Automatic Identification System (AIS) data show a qualitatively good association performance. Future developments of this work include the development of specific quantitative metrics to assess the association performance.

Recent studies have analyzed the chance of exploiting Global Navigation Satellite Systems (GNSS)-Reflectometry observables for maritime surveillance and sea target detection. In this paper, we provide a feasibility study of the ship detection problem using spaceborne GNSS-R data. The analysis is performed via the evaluation of the signal-to-noise-plus-clutter-ratio and signal-to-noise-ratio relevant to an isolated ship target in open sea. In particular, we investigated the impact of the GNSS-R acquisition geometry and radar signal polarization. The influence of sea state and ship orientation is assessed as well. The analysis is based on a sound theoretical electromagnetic model of the bistatic radar cross section of the ship target. The analysis clearly shows the benefits of 1) the backscattering configuration with respect to the conventional forward-scattering one and 2) the RHCP receiving channel w.r.t. the conventional LHCP one, used in sea surface analysis. However, the ship orientation and the sea state still play a key role in ship detectability.

Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic environment learning strategy to track both the covariance matrix and its class; the class represents a set of structured covariance matrices. We assume that the posterior distribution of the covariance given the class, is basically a mixture of inverse Wishart, while the class posterior distribution evolves according to a Markov chain. The proposed multi-class inverse Wishart mixture filter is shown to outperform the class-clairvoyant maximum likelihood estimator in terms of covariance estimate accuracy, as well as the Bayesian information criterion rule in terms of classification performance.

Inspired by the fair regularity of the motion of ships, we present a method to derive a representation of the commercial maritime traffic in the form of a graph, whose nodes represent way-point areas, or regions of likely direction changes, and whose edges represent navigational legs with constant cruise velocity. The proposed method is based on the representation of a ship's velocity with an Ornstein-Uhlenbeck process and on the detection of changes of its long-run mean to identify navigational way-points. In order to assess the graph representativeness of the traffic, two performance metrics are introduced, leading to distinct graph construction criteria. Finally, the proposed method is validated against real-world Automatic Identification System data collected in a large area.

In this paper, a comparative study of spaceborne Global Navigation Satellite System (GNSS)-Reflectometry for ship detection applications is provided. The analysis is conducted by evaluating the impact of 1) the acquisition geometry and 2) the received signal polarization on ship detectability in GNSS-R data. In particular, the backscattering acquisition geometry is demonstrated to be more suitable for ship detection applications, thus allowing for the detection of 20 m-length ships. Even very large ships are hardly detectable in the conventional forward-scattering geometry. Moreover, receiving right-hand circular polarization is demonstrated to provide significant improvements of the signal-to-noise-plus-clutter with respect to the conventional left-hand circular polarization channel, conventionally exploited in GNSS-R remote sensing. The study is based on a numerical tool for the bistatic radar cross section of the ship, which is presented in a companion paper.

This paper presents a solution to the problem of sequential joint anomaly detection and tracking of a target subject to switching unknown path deviations. Based on a dynamic model described by Ornstein-Uhlenbeck (OU) stochastic processes, the anomaly is represented by a target (e.g., a marine vessel) that deviates from a preset route by changing its nominal mean velocity. The Random Finite Set (RFS) framework is used to represent the switching nature of target's anomalous behavior in the presence of spurious measurements and detection uncertainty. Combining these two ingredients, the problem of jointly detecting target's path deviations and estimating its kinematic state can be formulated within the Bayesian framework, and analytically solved by means of a hybrid Bernoulli filter that sequentially updates the joint posterior density of the unknown OU velocity input (a Bernoulli RFS) and of the target's state random vector. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, in a simulated scenario of vessel tracking for maritime traffic monitoring.

A novel anomaly detection procedure is presented, based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity. In order to hide this behavior, the vessel switches off its Automatic Identification System (AIS) device for a certain time, and then tries to revert to the previous nominal velocity. The decision that has to be taken is either declaring that a deviation happened or not, relying only upon two consecutive AIS contacts. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter of the vessel is the core of the proposed approach and enables for the solution of the anomaly detection problem.

In this work, we consider the problem of algorithmically predicting rendezvous among vessels based on their trajectory forecasts in a maritime environment. The problem is treated as hypothesis testing on the expected value of the distance between trajectories. We relate this quantity to the first and second degree Wasserstein distances between trajectory forecast distributions. These distributions are obtained using integrated Ornstein-Uhlenbeck process models with the trajectory measurements collected so far. Building upon these results, we propose an algorithm which traverses the trajectories observed so far for detecting rendezvous over a rolling time horizon. We demonstrate the efficacy of the proposed algorlthm using simulations.

The chance of exploiting the Global Navigation Satellite Systems-Reflectometry (GNSS-R) remote sensing technology in the field of maritime surveillance and ship traffic monitoring has been explored in the very recent past. The conventional bistatic GNSS-R is based on the acquisition of GNSS navigation messages in a forward-scattering geometry, where the investigated region surrounds the specular reflection point. The peculiarities of the ship detection problem, where man-made dihedral structures come into play, make the conventional configuration unsuitable for such an application, especially from spaceborne platforms. Very recently, the backscattering configuration has been demonstrated to greatly enhance the presence of targets within GNSS-R delay-Doppler maps (DDM). In this paper, a thorough comparison of the forward- and back-scattering configurations for ship detection application from spaceborne GNSS-R instruments is presented in order to quantitatively evaluate the benefits of the backscattering configuration with respect to the conventional one. The analysis is performed by computing the signal-to-noise-plus-clutter ratio in both configurations as a function of the sea state, ship orientation and radar look angle.

We describe how we leveraged best practices in big data processing pipeline design and visual analytics to prototype the Maritime Patterns-of-Life Information Service (MPoLIS), an information product currently under development at the NATO Centre for Maritime Research and Experimentation (CMRE). MPoLIS supports the maritime industry, governments, and international organizations with visual analytics on vessel traffic in seaports. It addresses three main requirements: a) storing and processing large amounts of data; b) on-demand availability of statistical summaries of vessel traffic in ports; c) intuitive and interactive interface for subject matter experts (SMEs) in the maritime domain. MPoLIS has contributed to building a data-driven, self-service analytics culture within NATO and has been sanctioned for use in support of maritime situational awareness (MSA) in ongoing NATO operations.

Seaports play a vital role in the global economy, as they operate as the connection corridors to all other modes of transport and as engines of growth for the wider region. But ports today are faced with numerous unique challenges and for them to remain competitive, significant investments are required. In support of greater transparency in policy making, decisions regarding investment need to be supported by data-driven intelligence. It is often an overlooked fact that seaports do not remain static over time; such spatial units often evolve according to environmental patterns both in size but also connectivity and operational capacity. As such any valid decision making regarding port investment and policy making, essentially needs to take into account port evolution over time and space. In this work, we leverage the huge amounts of vessel data that are progressively becoming available through the Automatic Identification System (AIS) and distributed machine learning to define a seaport's extended area of operation. Specifically, we present our adaptation of the well-known KDE algorithm to the map-reduce paradigm, and report results on the port of Shanghai.

Seaports are spatial units that do not remain static over time. They are constantly in flux, evolving according to environmental and connectivity patterns both in size and operational capacity. As such any valid decision making regarding port investment and policy making, essentially needs to take into account port evolution over time and space, thus, accurately defining a seaport's exact location, operational boundaries, capacity, connectivity indicators, environmental impact and overall throughput. In this work, we apply a data driven approach to defining a seaport's extended area of operation based on data collected though the Automatic Identification System (AIS). Specifically, we present our adaptation of the well-known KDE algorithm to the MapReduce paradigm, and report results on the port of Rotterdam.

Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available through the Automatic Identification System (AIS). A critical challenge of scaling computational MSA to big data regimes is integrating the core learning algorithms with big data storage modes and data models. To address this challenge, we report results from our experimentation with MongoDB, a NoSQL document-based database which we test as a supporting platform for computational MSA. We experiment with a document model that avoids database joins when linking position and voyage AIS vessel information and allows tuning the database index and document sizes in response to the AIS data rate. We report results for the AIS data ingested and analyzed daily at the NATO Centre for Maritime Research and Experimentation (CMRE).

This paper presents the recent developments of an Optimal Path Planning — Decision Support System (OPP-DSS). The designed framework is based on multi-objective optimization algorithms providing a set of Pareto efficient solutions representing a trade-off among mission objectives. Meteorological and Oceanographic (METOC) and Automatic Identification System (AIS) vessel traffic data are integrated and exploited inside the planning process to improve surveillance in piracy risk areas. Tests in an operational scenario with real-world data provide indication of the effectiveness of the approach.