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Improving maritime traffic safety by applying routes exchange and automatic relevant radar data exchange

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Salous M. , Müller H. , Bolles A. , Hahn A.

tom nr 44 (116)

90--98

The e-navigation strategy of the International Maritime Organization (IMO) aims to improve the safety of maritime traffic by increasing cooperation between several maritime stakeholders. The COSINUS (Bolles et al., 2014) project contributes to such a strategy by enabling an automated data exchange (observations, routes and maneuver plans) between ship-side and shore-side navigational systems, developing useful sensor fusion applications upon the new information available from data exchange and introducing new Human Machine Interfaces (HMIs) to support the users of navigation systems. The project shows potential for improvement in maritime traffic safety by ensuring continuous awareness to all participants involved through sensor fusion applications, i.e. by providing all participants (mobile and stationary navigation systems) with a complete view at all times. These applications include detection of critical situations like radar shadowing areas, early and accurate prediction of potential collisions or closest point of approach (CPA) based on the exchanged routes, and improving the accuracy of radars by ensuring high quality data for obstructed or far away routes. The new HMI concepts introduced within the COSINUS project aim at highlighting critical maritime traffic situations. Thus, the users of such navigation systems supported with COSINUS facilities can easily detect such critical situations and react efficiently to avoid collisions, possible crowded areas and inefficient routes.

Parameter identification of ship maneuvering models using recursive least square method based on support vector machines

100%

Zhu M. , Hahn A. , Wen Y. , Bolles A.

tom Vol. 11, no. 1

23--29

Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.