Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques

摘要

To deal with highly time complexity and unstable assessments for conflicting evidences from various navigation factors, we put forward an innovative assessment scheme of navigation risk based on the improved multi-source information fusion techniques. Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. The slack variable is adaptively computed based on the Euclidean distance ratio. Considering the unsatisfactory characteristics of the standard Dempster–Shafer evidence theory, the optimal combination rule is derived step by step. What"s more, the lowly dimensional Kalman filter is applied to forecast the navigation risk. Simultaneously, the time complexity of each technique is analyzed. With respect to the vessel navigation risk, the assessment results are provided to indicate the reliability and efficiency of the proposed scheme.