CNN and BILSTM-based ship trajectory prediction method

A technology of ship trajectory and prediction method, which is applied in the direction of neural learning method, neural architecture, biological neural network model, etc., can solve the problem of low accuracy of ship trajectory prediction, and achieve the effects of easy implementation, strong scalability, and improved accuracy

Pending Publication Date: 2022-03-08
HARBIN ENG UNIV +1
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Problems solved by technology

[0004] The present invention provides a ship trajectory prediction method based on CNN and BILSTM to solve the problem of low accuracy of ship trajectory prediction in complex water traffic environment

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  • CNN and BILSTM-based ship trajectory prediction method
  • CNN and BILSTM-based ship trajectory prediction method
  • CNN and BILSTM-based ship trajectory prediction method

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[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0043] A method for predicting ship trajectory based on CNN and BILSTM, comprising the following steps:

[0044] Step 1: Preprocessing the information data; the information data includes longitude, latitude, course and speed collected by the ship automatic identification system AIS;

[0045] The preprocessing of the step 1 is specifically to screen out the information in the ship automatic identification system AIS that has a greater impact on the ...

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Abstract

The invention discloses a ship trajectory prediction method based on a CNN and a BILSTM. The method comprises the following steps: 1, preprocessing information data; the information data comprises longitude, latitude, course and navigational speed acquired by a ship AIS (Automatic Identification System); 2, dividing the information data preprocessed in the step 1 into a training set and a test set; 3, importing the training set in the step 2 into a convolutional neural network (CNN) for feature extraction; 4, combining the features extracted in the step 3 with data of the training set to form input data of trajectory prediction; 5, importing the input data in the step 4 into a BILSTM neural network model for learning, and obtaining a ship motion rule model implied in the trajectory data; and 6, predicting the trajectory of the ship by using the model in the step 5. The method is used for solving the problem that the ship trajectory prediction accuracy is low in a complex water traffic environment.

Description

technical field [0001] The invention belongs to the technical field of time series prediction; in particular, it relates to a ship track prediction method based on CNN and BILSTM. Background technique [0002] With the continuous development of shipping, it is more urgent to strengthen ship safety management and key target monitoring. In many coastal and port waters with dense traffic and complicated conditions, the accuracy and effectiveness of early warning of marine traffic accidents is also particularly important. Knowing the direction information of ships in advance can effectively reduce the occurrence of marine traffic accidents such as ship running aground and collision. Ship trajectory analysis can obtain useful information on sea lanes and ship behavior patterns. Due to the different characteristics of ship navigation and vehicle driving, there is no obvious road network constraint, the track is more random, and the prediction is more difficult. The traditional ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/049G06N3/084G06N3/045G06N3/044
Inventor 李芃陈赛孙宏放张兰勇李奕霏刘洪丹
Owner HARBIN ENG UNIV
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