Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

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 (LSTM) 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 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 Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) 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.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\sim \exp\left(-n\,I + o(n) \right)$, where $n$ is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and $I$ is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate $I$, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for $n$ large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of $n$. Furthermore, approximate error probability curves $\sim \zeta_n \exp\left(-n\,I \right)$ are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where $\zeta_n$ represents the most representative sub-exponential terms of the error probabilities

COVID-19 Impact on Global Maritime Mobility

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 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: 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 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, 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. This study is unprecedented for the uniqueness and completeness of the employed dataset, which comprises a trillion AIS messages broadcast worldwide by 50000 ships, a figure that closely parallels the documented size of the world merchant fleet

Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves

The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restriction measures adopted by political authorities, such as national lockdowns, have not prevented the outbreak of new pandemic's waves. In this article, we propose an integrated detection-estimation-forecasting framework that, using publicly available data published by the national authorities, is designed to: (i) learn relevant features of the epidemic (e.g., the infection rate); (ii) detect as quickly as possible the onset (or the termination) of an exponential growth of the contagion; and (iii) reliably forecast the epidemic evolution. The proposed solution is validated by analyzing the COVID-19 second and third waves in the USA.Comment: Submitted to IEEE Communications Magazine, feature topic "Networking Technologies to Combat the COVID-19 Pandemic

Application of Hidden Markov Models to Analyze, Group and Visualize Spatio-Temporal COVID-19 Data

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

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Simulation-Based Feasibility Analysis of Ship Detection Using GNSS-R Delay-Doppler Maps

In this article, we carry out a simulation analysis of ship detection via Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM). The GNSS-R DDM simulator used here is a modified version of an algorithm conceived for the generation of GNSS-R DDMs of the sea surface. The new algorithm is based on an analytical model for the radar cross section of ships and is able to properly account for the presence of ship targets within the scene. The proposed GNSS-R DDM simulator is, then, exploited for assessing the viability of GNSS-R in ship detection applications at low incidence angles, where the adopted scattering models provide accurate results. The aim of the implemented simulation setup is to analyze what are the preferable conditions for ship detection using standard GNSS-R signal processing chain receiver and compare typical forward left-hand circularly polarized GNSS-R systems with nonstandard backward right-hand circularly polarized (RHCP) GNSS-R. The simulation study is two fold: First, detection performance is evaluated at spaceborne and airborne altitudes for both polarization channels under favorable detection conditions. Then, visibility of ship targets is assessed in terms of their location within the DDM. Simulation results show that ship detection is problematic when using satellite data, whereas interesting results are achieved at airborne altitudes, provided that the aircraft is approximately between the GNSS satellite and the target, and that appropriate RHCP polarization is probed. In such configurations, signal-to-noise-ratios larger than 10 dB are obtained with airborne receivers collecting the RHCP signal