Incorporation of Deep Kernel Convolution into Density Clustering for Shipping AIS Data Denoising and Reconstruction

• Published in: Journal of Marine Science and Engineering
• Main Authors: Zhang, J, Ren, X, Li, H, Yang, Z
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2022
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); Arctic; Arctic Ocean; Dover
• Online Access: http://researchonline.ljmu.ac.uk/id/eprint/17669/; https://researchonline.ljmu.ac.uk/id/eprint/17669/1/jmse-10-01319-v4%20%281%29.pdf; https://doi.org/10.3390/jmse10091319

Automatic Identification System (AIS) equipment can aid in identifying ships, reducing ship collision risks and ensuring maritime safety. However, the explosion of massive AIS data has caused increasing data processing challenges affecting their practical applications. Specifically, mistakes, noise, and missing data are presented during AIS data transmission and encoding, resulting in poor data quality and inaccurate data sources that negatively impact maritime safety research. To address this issue, a robust AIS data denoising and reconstruction methodology was proposed to realise the data preprocessing for different applications in maritime transportation. It includes two parts: Density-Based Spatial Clustering of Applications with Noise based on Deep Kernel Convolution (DBSCANDKC) and the reconstruction method, which can extract high-quality AIS data to guarantee the accuracy of the related maritime research. Firstly, the kinematics feature was employed to remove apparent noise from the AIS data. The square deep kernel convolution was then incorporated into density clustering to find and remove possibly anomalous data. Finally, a piecewise cubic spline interpolation approach was applied to construct the missing denoised trajectory data. The experiments were implemented in the Arctic Ocean and Strait of Dover to demonstrate the effectiveness and performance of the proposed methodology in different shipping environments. This methodology makes significant contributions to future maritime situational awareness, collision avoidance, and robust trajectory development for safety at sea.