DOI: 10.57760/sciencedb.18596
title: Multisource Track Association Dataset (MTAD) Based on the Global AIS
title: 基于全球AIS的多源航迹关联数据集（MTAD）

description: Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: CUI Yaqi, XU Pingliang, GONG Cheng, YU Zhouchuan, ZHANG Jianting, YU Hongbo, DONG Kai. Multisource Track Association Dataset Based on the Global AIS[J]. Journal of Electronics & Information Technology, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202Authors: Ren Junyu, Yu Ningning, Zhou Chengwei, Shi Zhiguo, Chen JimingAuthor Unit:Institute of Information Fusion, Naval Aviation University91001 Unit91001 UnitCorrespondent: XU Pingliang，xu_pingliang@163.comOriginal link：基于全球AIS的多源航迹关联数据集Funds: The National Natural Science Foundation of China (61790554, 62001499, 62171453)Abstract: Data, algorithms, and hash rates are the three thrust forces for developing artificial intelligence. Considering the urgent demand for research on the intelligent association algorithm and the difficulty of obtaining track data from multi-radar collaborative observation and addressing the problem of missing track association dataset, a Multi-source Track Association Dataset (MTAD) is constructed in this study. MTAD is based on automatic identification system trajectory data after processing grid division, automatic interruption, and error adding. The dataset includes two parts, namely, the training dataset and the test dataset, with more than 1 million tracks. The train and test datasets contain 5000 and 1000 scene samples, respectively. Each scene sample consists of several to hundreds of tracks, covering various movement patterns, target types, and duration times. In addition, the constructed MTAD is further visualized and analyzed, and the characteristics of tracks in each grid are studied in detail, demonstrating the richness, rationality, and effectiveness of the MTAD. The indicators and baseline results of the association are obtained. This dataset has already been used as a dedicated dataset for the Navy’s “Golden Dolphin” Cup competition.
description: Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: CUI Yaqi, XU Pingliang, GONG Cheng, YU Zhouchuan, ZHANG Jianting, YU Hongbo, DONG Kai. Multisource Track Association Dataset Based on the Global AIS[J]. Journal of Electronics & Information Technology, 2023, 45(2): 746-756. doi: 10.11999/JEIT221202Authors: Ren Junyu, Yu Ningning, Zhou Chengwei, Shi Zhiguo, Chen JimingAuthor Unit:Institute of Information Fusion, Naval Aviation University91001 Unit91001 UnitCorrespondent: XU Pingliang，xu_pingliang@163.comOriginal link：基于全球AIS的多源航迹关联数据集Funds: The National Natural Science Foundation of China (61790554, 62001499, 62171453)Abstract: Data, algorithms, and hash rates are the three thrust forces for developing artificial intelligence. Considering the urgent demand for research on the intelligent association algorithm and the difficulty of obtaining track data from multi-radar collaborative observation and addressing the problem of missing track association dataset, a Multi-source Track Association Dataset (MTAD) is constructed in this study. MTAD is based on automatic identification system trajectory data after processing grid division, automatic interruption, and error adding. The dataset includes two parts, namely, the training dataset and the test dataset, with more than 1 million tracks. The train and test datasets contain 5000 and 1000 scene samples, respectively. Each scene sample consists of several to hundreds of tracks, covering various movement patterns, target types, and duration times. In addition, the constructed MTAD is further visualized and analyzed, and the characteristics of tracks in each grid are studied in detail, demonstrating the richness, rationality, and effectiveness of the MTAD. The indicators and baseline results of the association are obtained. This dataset has already been used as a dedicated dataset for the Navy’s “Golden Dolphin” Cup competition.