A novel fitting model for practical AIS abnormal data repair in inland river

Abstract

Affected by the environment of inland waterway, an Automatic Identification System (AIS) collects lots of abnormal data, which significantly reduces the inland river navigation performance using AIS data. To this end, this paper aims to restore the AIS data by repairing the lost data points. By analysing enormous abnormal AIS data, the abnormal data were firstly divided into three types, i.e., the erroneous data, short-time lost data, and long-time lost data. Then, a cubic spline interpolation method was employed to deal with the erroneous data and short-time lost data. Meanwhile, a least square support vector machine method was utilized to repair the long-time lost data. Finally, field experiments were carried out to validate the applicability of the proposed method, and it is shown that the fitting model can repair the AIS data with an accuracy of more than 90 %.