Ship trajectory clustering analysis method based on improved DBSCAN algorithm

A ship trajectory and clustering analysis technology, applied in the field of ship navigation safety, can solve the problems of long time consumption of ship trajectory clustering, inaccurate trajectory classification, poor robustness and adaptability, etc. Adaptability, reducing time consumption, avoiding the effect of accidents

Active Publication Date: 2019-10-08
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

[0003] The DBSCAN algorithm is usually used for cluster analysis of ship trajectories, but the accuracy of the existing DBSCAN algorithm measurement is low, the obtained

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  • Ship trajectory clustering analysis method based on improved DBSCAN algorithm
  • Ship trajectory clustering analysis method based on improved DBSCAN algorithm
  • Ship trajectory clustering analysis method based on improved DBSCAN algorithm

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[0047] In order to facilitate those skilled in the art to better understand the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0048] Such as figure 1 As shown, a kind of ship trajectory cluster analysis method based on the improved DBSCAN algorithm described in this embodiment comprises the following steps:

[0049] Step S1, extract valid ship trajectory data from the AIS database.

[0050] Step S2, using the fusion distance MD to measure the trajectory similarity, and obtain the shortest super-trajectory after the fusion between the trajectories.

[0051] Specifically, as figure 2 As shown, the specific method of using the fusion distance MD to measure the trajectory similarity to obtain the shortest supertrajectory after fusion between trajectories is as follows: Ass...

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Abstract

The invention discloses a ship trajectory clustering analysis method based on an improved DBSCAN algorithm. The ship trajectory clustering analysis method comprises the following steps: S1, extractingeffective ship trajectory data from an AIS database; S2, carrying out trajectory similarity measurement by adopting a fusion distance MD, and obtaining a shortest super trajectory fused between trajectories; S3, calculating the length of the shortest super track by adopting the secondary time O (mn); S4, obtaining a fusion distance MD from the length of the track; S5, determining global parameters of the improved DBSCAN algorithm; S6, scanning the whole track segment data set through an improved DBSCAN algorithm to obtain a cluster set; and S7, for the obtained spatial motion mode, if a new track conforms to one of the ship operation modes, considering the track as a normal track. According to the ship trajectory clustering analysis method, the measurement precision is improved; and compared with a traditional DBSCAN algorithm, the time consumption is reduced while a multi-density data set can be well processed, and the robustness and adaptability are good, and tracks are correctly classified.

Description

technical field [0001] The invention relates to the technical field of ship navigation safety, in particular to a ship track cluster analysis method based on an improved DBSCAN algorithm. Background technique [0002] With the development of the economy, the enhancement of waterway transportation and international trade has led to the rise of maritime traffic, and at the same time, a large number of ship motion trajectories have been generated, which is of great significance to the analysis and supervision of global maritime transportation. Nowadays, with the development of technology, the Automatic Identification System (AIS) is introduced to track ships and monitor maritime affairs. It can cooperate with the Global Positioning System (GPS) to exchange a series of ship dynamic and static information such as position, course, and speed with other ships and AIS base stations. Facilitates tracking and monitoring of vessel movements at sea. Through these AIS data, mining the s...

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Application Information

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IPC IPC(8): G06F16/906G06K9/62G06Q50/30
CPCG06F16/906G06Q50/30G06F18/2321G06F18/22G06F18/25
Inventor 陈姚节桂飞周海徐进
Owner WUHAN UNIV OF SCI & TECH
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