A detection and restoration approach for vessel trajectory anomalies based on AIS

Abstract

In view of the continuous increase in the amount of AIS data at sea and the existence of more abnormal points, it is difficult to construct ship trajectories based on AIS data. Aiming at this problem, a new method for identifying and repairing abnormal points in trajectories only based the AIS data of the ship itself is proposed. Longitude and latitude, speed, acceleration, direction and other parameters are comprehensively used to identify and repair the abnormal points in the method proposed. Compared with the methods based on single location data, it can effectively reduce the missed judgement of outliers. Compared with the methods based on trajectories clustering to judge singular point, this method does not require the data of historical trajectories to expand the application scope. The cubic spline method is used to interpolate points for the discontinuous segments to further improve the continuity and integrity of the ship trajectory. The results of AIS data processing and analysis on ships in actual sea areas verify the feasibility and effectiveness of the proposed method.

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

“…Qin et al [19] address the previous algorithms that do not consider the dynamic information of vessel operation; it is difficult to repair the curve of the ship trajectory, resulting in low accuracy, so they improved the linear interpolation algorithm and proposed a two-way iteration and weighted average of the trajectory repair iterative algorithm, which effectively takes into account the dynamic information of the ship, thus improving the restoration of the trajectory accuracy and at the same time compressing the algorithm execution time. Zhang et al [20] focused on single-ship AIS data and proposed a trajectory anomaly identification and repair method, which utilizes the parameter information of the AIS data itself to determine the anomalies and then repairs the trajectory anomalies by using the cubic spline interpolation method, which is effective in eliminating the abnormal data mutations of the parameters to make the repaired trajectory changes smoother. Also, Zhang et al [21] proposed a vector analysisbased vessel trajectory anomaly detection and track repair method, which analyzes the characteristics of AIS raw data to establish the trajectory basis vectors, uses the basis vectors as the basis for classifying the vessel trajectory categories, and finally reconstructs the trajectory according to the unused trajectory line type categories by using different interpolation methods, respectively.…”

Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

“…Before fishing behavior detection, track quality inspection should be completed since false alerts and missing track points can be found in real AIS data. First, the abnormalities in parameters, such as longitude, latitude, speed, acceleration, and direction, in the AIS data will be identified and fixed [26]. It is of little importance to determine the fishing behavior of fishing vessels in locations with major missing track points because a fishing activity typically lasts for 3 to 15 h. In this study, the boxplot method's use of the interquartile range to identify outliers is utilized.…”