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Alvarez A.,Center for Maritime Research and Experimentation CMRE
Deep-Sea Research Part I: Oceanographic Research Papers | Year: 2015

The determination of non-directional and directional sea wave spectra is attempted by analyzing the dynamical response of a surfaced Slocum glider. The method makes use of the glider heave motion to infer non-directional properties of the wave spectra. In addition, surge and sway motions are considered to derive wave directionality. The transfer functions for heave, surge and sway for a surfaced Slocum glider has been computed to determine the impact of the body geometry/inertia and the angle of incidence on the response of the surfaced platform when excited by regular waves. Numerical results show that for wave periods longer than 6. s, motions measured at the glider platform can be assumed equivalent to motions experimented by the water parcels at the sea surface. Spectral information about sea state conditions can then be directly derived from the measurement of glider responses. Results also reveal a natural period of the surfaced glider at around 5. s. A series of field experiments were conducted during March 7th, May 17th and May 27th of 2013 in a marine region off-shore La Spezia, to validate the methodology. Specifically, the wave spectra derived from a Slocum glider equipped with a set of accelerometers was compared against the values provided by a Datawell Waverider Mk3 moored in the vicinity of the glider deployment. Agreement has been found between the non-directional estimates from the glider and the values measured by the waverider. Regarding wave directionality, glider estimates of peak direction agree well with those reported by the waverider. More significant differences are found between the directional spread estimates. © 2015 NATO/CMRE. Source


Ferri G.,Center for Maritime Research and Experimentation CMRE | Cococcioni M.,University of Pisa | Alvarez A.,Center for Maritime Research and Experimentation CMRE
Sensors (Switzerland) | Year: 2015

This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in the last decade, consists in planning the paths of gliders to minimize a specific criterion pertinent to the phenomenon under investigation. Different criteria (e.g., A, G, or E optimality), used in geosciences to obtain an optimum design, lead to different sampling strategies. In particular, the A criterion produces paths for the gliders that minimize the overall level of uncertainty over the area of interest. However, there are commonly operative situations in which the marine scientists may prefer not to minimize the overall uncertainty of a certain area, but instead they may be interested in achieving an acceptable uncertainty sufficient for the scientific or operational needs of the mission. We propose and discuss here an approach named sampling on-demand that explicitly addresses this need. In our approach the user provides an objective map, setting both the amount and the geographic distribution of the uncertainty to be achieved after assimilating the information gathered by the fleet. A novel optimality criterion, called A η , is proposed and the resulting minimization problem is solved by using a Simulated Annealing based optimizer that takes into account the constraints imposed by the glider navigation features, the desired geometry of the paths and the problems of reachability caused by ocean currents. This planning strategy has been implemented in a Matlab toolbox called SoDDS (Sampling on-Demand and Decision Support). The tool is able to automatically download the ocean fields data from MyOcean repository and also provides graphical user interfaces to ease the input process of mission parameters and targets. The results obtained by running SoDDS on three different scenarios are provided and show that SoDDS, which is currently used at NATO STO Centre for Maritime Research and Experimentation (CMRE), can represent a step forward towards a systematic mission planning of glider fleets, dramatically reducing the efforts of glider operators. © 2015 by the authors; licensee MDPI, Basel, Switzerland. Source


Pallotta G.,Center for Maritime Research and Experimentation CMRE | Jousselme A.-L.,Center for Maritime Research and Experimentation CMRE
2015 18th International Conference on Information Fusion, Fusion 2015 | Year: 2015

Discovering anomalies at sea is one of the critical tasks of Maritime Situational Awareness (MSA) activities and an important enabler for maritime security operations. This paper proposes a data-driven approach to anomaly detection, highlighting challenges specific to the maritime domain. This work builds on unsupervised learning techniques which provide models for normal traffic behaviour. A methodology to associate tracks to the derived traffic model is then presented. This is done by the pre-extraction of contextual information as the baseline patterns of life (i.e., routes) in the area under investigation. In addition to a brief description of the approach to derive the routes, their characterization and representation is presented in support of exploitable knowledge to classify anomalies. A hierarchical reasoning is proposed where new tracks are first associated to existing routes based on their positional information only and 'off-route' vessels' are detected. Then, for on-route vessels further anomalies are detected such as 'speed anomaly' or 'heading anomaly'. The algorithm is illustrated and assessed on a real-world dataset supplemented with synthetic abnormal tracks. © 2015 IEEE. Source


Cazzanti L.,Center for Maritime Research and Experimentation CMRE | Pallotta G.,Center for Maritime Research and Experimentation CMRE
MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World | Year: 2015

This paper discusses machine learning and data mining approaches to analyzing maritime vessel traffic based on the Automated Information System (AIS). We review recent efforts to apply machine learning techniques to AIS data and put them in the context of the challenges posed by the need for both algorithmic performance generalization and interpretability of the results in real-world maritime Situational Awareness settings. We also present preliminary work on discovering and characterizing vessel stationary areas using an unsupervised spatial clustering algorithm. © 2015 IEEE. Source


Vermeij A.,Center for Maritime Research and Experimentation CMRE | Munafo A.,Center for Maritime Research and Experimentation CMRE
MTS/IEEE OCEANS 2015 - Genova: Discovering Sustainable Ocean Energy for a New World | Year: 2015

This paper proposes an algorithm for real-time clock synchronisation in underwater acoustic networks. The algorithm models modem clocks as linear functions and embeds the data necessary for the clock synchronisation procedure as time stamps that can be communicated in the payload of later messages and only when necessary, hence limiting the communication overhead. The proposed solution takes explicitly into account the limitations of the acoustic communication channel and network node mobility. Specific implementation issues are discussed for its deployment within operational networks. Experimental results are given from the COLLAB-NGAS14 campaign, held in October 2014 off the coast of West Italy. © 2015 IEEE. Source

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