Maritime anomaly detection in ferry tracks

This paper proposes a methodology for the automatic detec- tion of anomalous shipping tracks traced by ferries. The ap- proach comprises a set of models as a basis for outlier detec- tion: A Gaussian process (GP) model regresses displacement information collected over time, and a Markov chain based detector makes use of the direction (heading) information. GP regression is performed together with Median Absolute Devi- ation to account for contaminated training data. The method- ology utilizes the coordinates of a given ferry recorded on a second by second basis via Automatic Identification System. Its effectiveness is demonstrated on a dataset collected in the Solent area

Detection of Abnormal Vessel Behaviours Based on AIS Data Features Using HDBSCAN+

 Achieving maritime security is challenging due to the vastness and complexity of the domain. Monitoring all Achieving maritime security is challenging due to the vastness and complexity of the domain. Monitoringall vessels that use this medium is humanly impossible but is needed for law enforcement. This paper proposes amachine learning solution based on HDBSCAN+ to classify the movements of vessels into ‘normal’ or ‘abnormal’.This classification reduces the number of vessels that have to be monitored by law enforcement agencies to amanageable size. To date, AIS is the primary source of information that can represent vessel movements andenable the detection of maritime anomalies. The proposed model uses latitude, longitude, type of vessel, courseand speed as features of the AIS data for analysis. The performance of the proposed model is validated against the marine incidents reported by Information Fusion Centre-Indian Ocean Region (IFC-IOR). The proposed model has successfully detected the incidents reported by IFC-IOR

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Tese de doutoramento, Estatística e Investigação Operacional (Optimização), Universidade de Lisboa, Faculdade de Ciências, 2018Mass rescue operations (MRO) in maritime areas, particularly in ocean areas, are a major concern for the authorities responsible for conducting search and rescue (SAR) activities. A mass rescue operation can be defined as a search and rescue activity characterized by the need for immediate assistance to a large number of persons in distress, such that the capabilities normally available to search and rescue are inadequate. In this dissertation we deal with a mass rescue operation within ocean areas and we consider the problem of rescuing a set of survivors following a maritime incident (cruise ship, oil platform, ditched airplane) that are drifting in time. The recovery of survivors is performed by nearby ships and helicopters. We also consider the possibility of ships capable of refuelling helicopters while hovering which can extend the range to which survivors can be rescued. A linear binary integer formulation is presented along with an application that allows users to build instances of the problem. The formulation considers a discretization of time within a certain time step in order to assess the possibility of travelling along different locations. The problem considered in this work can be perceived as an extension of the generalized vehicle routing problem (GVRP) with a profit stance since we may not be able to recover all of the survivors. We also present a look ahead approach, based on the pilot method, to the problem along with some optimal results using state of the art Mixed-integer linear programming solvers. Finally, the efficacy of the solution from the GVRP is estimated for a set of scenarios that combine incident severity, location, traffic density for nearby ships and SAR assets availability and location. Using traffic density maps and the estimated MRO efficacy, one can produce a combined vulnerability map to ascertain the quality of response to each scenario.Marinha Portuguesa, Plano de Atividades de Formação Nacional (PAFN

Maritime anomaly detection in ferry tracks

This paper proposes a methodology for the automatic detec- tion of anomalous shipping tracks traced by ferries. The ap- proach comprises a set of models as a basis for outlier detec- tion: A Gaussian process (GP) model regresses displacement information collected over time, and a Markov chain based detector makes use of the direction (heading) information. GP regression is performed together with Median Absolute Devi- ation to account for contaminated training data. The method- ology utilizes the coordinates of a given ferry recorded on a second by second basis via Automatic Identification System. Its effectiveness is demonstrated on a dataset collected in the Solent area

Maritime anomaly detection in ferry tracks

This paper proposes a methodology for the automatic detec- tion of anomalous shipping tracks traced by ferries. The ap- proach comprises a set of models as a basis for outlier detec- tion: A Gaussian process (GP) model regresses displacement information collected over time, and a Markov chain based detector makes use of the direction (heading) information. GP regression is performed together with Median Absolute Devi- ation to account for contaminated training data. The method- ology utilizes the coordinates of a given ferry recorded on a second by second basis via Automatic Identification System. Its effectiveness is demonstrated on a dataset collected in the Solent area