Anomaly Detection and Restoration for AIS Raw Data

Abstract

With the wide application of location detection sensors in maritime surveillance, a large amount of raw automatic identification system (AIS) data is produced by many moving ships. Anomaly detection and restoration of the big AIS data are important issues in marine data mining, because they offer a reliable support to users to mining the behaviors of ships. This paper develops a novel approach to detect anomaly AIS data based on the ships’ maneuverability, such as the maximum acceleration, the minimum acceleration, the maximum distance, and the maximum angular displacement, which were designed to detect the anomaly AIS data. Furthermore, the performance of the developed approach is compared with that of Daiyong-Zhang’s method and Behrouz-Haji-Soleimani’s method to assess its detection efficiency. The results show that the proposed approach can be applied to easily extract the abnormal data. Finally, based on the developed approach to detect the anomaly data and cubic spline interpolation method to restore the AIS data, experiments are conducted on the AIS data of Xiamen Port of Fujian Province, China, that prove to be effective for marine intelligence research.