Unsupervised Maritime Traffic Graph Learning with Mean-Reverting Stochastic Processes

被引：0

作者：

Coscia, Pasquale ^{[1
]}

Braca, Paolo ^{[2
]}

Millefiori, Leonardo M. ^{[2
]}

Palmieri, Francesco A. N. ^{[1
]}

Willett, Peter ^{[3
]}

机构：

[1] Univ Campania Luigi Vanvitelli, Dipartimento Ingn, Aversa, Italy

[2] NATO STO Ctr Maritime Res & Expt CMRE, La Spezia, Italy

[3] Univ Connecticut, ECE Dept, Storrs, CT USA

来源：

2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2018年

关键词：

Ornstein-Uhlenbeck process; change detection; maritime traffic graph; graph learning; clustering; DBSCAN; real-world data; AIS; maritime situational awareness;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Inspired by the fair regularity of the motion of ships, we present a method to derive a representation of the commercial maritime traffic in the form of a graph, whose nodes represent way-point areas, or regions of likely direction changes, and whose edges represent navigational legs with constant cruise velocity. The proposed method is based on the representation of a ship's velocity with an Ornstein-Uhlenbeck process and on the detection of changes of its long-run mean to identify navigational way-points. In order to assess the graph representativeness of the traffic, two performance metrics are introduced, leading to distinct graph construction criteria. Finally, the proposed method is validated against real-world Automatic Identification System data collected in a large area.

引用

页码：1822 / 1828

页数：7

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Millefiori, Leonardo M.
Braca, Paolo
Bryan, Karna
Willett, Peter
[J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2016, 52 (05) : 2313 - 2330

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