Statistical Analysis of Massive AIS Trajectories Using Gaussian Mixture Models

被引：6

作者：

Hu, Bin ^{[1
]}

Liu, Ryan Wen ^{[1
,2
]}

Wang, Kai ^{[1
]}

Li, Yan ^{[1
]}

Liang, Maohan ^{[1
,2
]}

Li, Huanhuan ^{[1
,2
]}

Liu, Jingxian ^{[1
,2
]}

机构：

[1] Wuhan Univ Technol, Sch Nav, Wuhan 430063, Hubei, Peoples R China

[2] Wuhan Univ Technol, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Hubei, Peoples R China

来源：

2017 2ND INTERNATIONAL CONFERENCE ON MULTIMEDIA AND IMAGE PROCESSING (ICMIP) | 2017年

基金：

中国国家自然科学基金;

关键词：

automatic identification system; Gaussian mixture model; Expectation Maximization algorithm; statistical analysis; EM ALGORITHM;

D O I：

10.1109/ICMIP.2017.57

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Automatic Identification System (AIS) is an automatic tracking system which has been widely applied in the fields of intelligent transportation systems, e.g., collision avoidance, navigation, maritime supervision and management. Compare with other positioning systems, e.g., very high frequency (VHF) and radar, AIS can conquer the human errors and it is almost not affected by the external environment. To make better use of the AIS data, it is necessary to statistically analyze the massive AIS trajectories. The statistical results could make us better understand the potential properties of AIS trajectories. It is well known that most current practical applications are strongly dependent on the geometrical structures of AIS trajectories. In this paper, a Gaussian Mixture Model (GMM) is introduced to investigate the longitude and latitude differences of AIS trajectory data. The parameters of GMM are estimated using the Expectation Maximization (EM) algorithm. The experimental results have illustrated the superior performance of our proposed method.

引用

页码：113 / 117

页数：5

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