Browsing by Author "Millefiori, Leonardo"
Now showing items 1-20 of 23
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Adaptive filtering of imprecisely time-stamped measurements with application to AIS networks
Millefiori, Leonardo; Braca, Paolo; Bryan, Karna; Willett, Peter K. (CMRE, 2019/06)Driven by real-world issues in maritime surveillance, we consider the problem of estimating the target state from a sequence of observations that can be imprecisely time-stamped. That is, the time between two consecutive ... -
Anomaly detection and tracking based on mean-reverting processes with unknown parameters
Forti, Nicola; Millefiori, Leonardo; Braca, Paolo; Willett, Peter K. (CMRE, 2019/05)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 ... -
Automated port traffic statistics: from raw data to visualization
Cazzanti, Luca; Davoli, Antonio; Millefiori, Leonardo (CMRE, 2017/11)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 ... -
Bayesian multi-class covariance matrix filtering for adaptive environment learning
Braca, Paolo; Aubry, Augusto; Millefiori, Leonardo; De Maio, Antonio; Marano, Stefano (CMRE, 2019/05)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 ... -
Bayesian track-to-graph association for maritime traffic monitoring
Grasso, Raffaele; Millefiori, Leonardo; Braca, Paolo (CMRE, 2019/05)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 ... -
Consistent estimation of randomly sampled Ornstein-Uhlenbeck process long-run mean for long-term target state prediction
Millefiori, Leonardo; Braca, Paolo; Willett, Peter K. (CMRE, 2019/06)In this letter, we study the problem of estimating the long-run mean of the Ornstein-Uhlenbeck (OU) stochastic process and its effect on the long-term prediction of future vessel states, which is a crucial problem for ... -
Data driven vessel trajectory forecasting using stochastic generative models
Uney, Murat; Millefiori, Leonardo; Braca, Paolo (2019/05)In this work, we propose a data driven trajectory forecasting algorithm that utilizes both recorded historical and streaming trajectory observations. The algorithm performs Bayesian inference on a directed graph the walks ... -
Detecting anomalous deviations from standard maritime routes using the Ornstein-Uhlenbeck process
D'Afflisio, Enrica; Braca, Paolo; Millefiori, Leonardo; Willett, Peter K. (CMRE, 2019/05)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 ... -
A distributed approach to estimating sea port operational regions from lots of AIS data
Millefiori, Leonardo; Zissis, Dimitrios; Cazzanti, Luca; Arcieri, Gianfranco (CMRE, 2019/06)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 ... -
A document-based data model for large scale computational maritime situational awareness
Cazzanti, Luca; Millefiori, Leonardo; Arcieri, Gianfranco (CMRE, 2019/06)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 ... -
Hybrid Bernoulli filtering for detection and tracking of anomalous path deviations
Forti, Nicola; Millefiori, Leonardo; Braca, Paolo (CMRE, 2019/05)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) ... -
Long-term vessel kinematics prediction exploiting mean-reverting processes
Millefiori, Leonardo; Braca, Paolo; Bryan, Karna; Willett, Peter K. (CMRE, 2019/06)Long-term target state estimation of non-manoeuvring targets, such as vessels under way in open sea, is crucial for maritime security. The dynamics of non-manoeuvring targets is traditionally modelled with a white noise ... -
Maritime anomaly detection based on mean-reverting stochastic processes applied to a real-world scenario
D'Afflisio, Enrica; Braca, Paolo; Millefiori, Leonardo; Willett, Peter K. (CMRE, 2019/05)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 ... -
Maritime situational awareness use cases enabled by space-borne sensors
Millefiori, Leonardo; Vivone, Gemine; Braca, Paolo; Cazzanti, Luca; Bryan, Karna (CMRE, 2019/06)This paper discusses a technique for predicting a vessel?s position over a long time horizon with much lower uncertainty than current methods. Lowering the uncertainty of long-range prediction is a key challenge in maritime ... -
Model performance assessment for long-term vessel prediction using HFSW radar data
Vivone, Gemine; Millefiori, Leonardo; Braca, Paolo; Willett, Peter K. (CMRE, 2019/06)Vessels in open seas are seldom continuously observed. Thus, the problem of long-term vessel prediction becomes crucial. This paper focuses its attention on the performance assessment of the Ornstein- Uhlenbeck target ... -
Modeling vessel kinematics using a stochastic mean-reverting process for a long-term prediction
Millefiori, Leonardo; Braca, Paolo; Bryan, Karna; Willett, Peter K. (CMRE, 2019/06)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 ... -
Multiple Ornstein-Uhlenbeck processes for maritime traffic graph representation
Coscia, Pasquale; Braca, Paolo; Millefiori, Leonardo; Palmieri, Francesco A. N.; Willett, Peter K. (CMRE, 2019/05)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 ... -
Performance assessment of vessel dynamic models for long-term prediction using heterogeneous data
Vivone, Gemine; Millefiori, Leonardo; Braca, Paolo (CMRE, 2019/06)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 ... -
Prediction of rendezvous in maritime situational awareness
Uney, Murat; Millefiori, Leonardo; Braca, Paolo (CMRE, 2019/05)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 ... -
Proceedings of the Maritime Situational Awareness Workshop 2019
Jousselme, Anne-Laure; Braca, Paolo; Millefiori, Leonardo; De Rosa, Francesca; Zocholl, Maximilian; Uney, Murat; Iphar, Clément; Gaglione, Domenico; Soldi, Giovanni; Forti, Nicola; D'Afflisio, Enrica (CMRE, 2021-01)The Maritime Situational Awareness Workshop (MSAW) was held 08 - 10 October 2019 in Lerici, La Spezia (Italy). CMRE organized this workshop with the objective of fostering the crossfertilization of ideas from scientific ...