随着海上交通环境的日益复杂,海上智能交通成为人们研究的热点,其中,对船舶航迹的有效分析与控制是其中的关键技术。本文主要对船舶航迹分析方法进行研究,给出一种基于云平台规模样本条件下模拟训练船舶航迹点生成算法。为得到效果较好的航迹云图,本文借助云平台扩大训练样本集,然后利用正态云分析算法模拟生成与实际船舶航迹相符合的航迹云图。
With the increasing complexity of the marine traffic environment, the intelligent transportation became a hot topic in the research of the sea, and the effective analysis and control of the ship's track was the key technology. In this paper, we mainly studied the method of ship track analysis, and proposed a simulation training ship's track point generation algorithm based on cloud platform scale samples. In order to get better track image, this paper expanded the training sample set with the help of cloud platform, and then used the normal cloud analysis algorithm to generate the track cloud image which was in accordance with the actual ship's track.
2017,39(1A): 28-30 收稿日期:2016-11-20
DOI:10.3404/j.issn.1672-7619.2017.1A.010
分类号:U665.26A
基金项目:重庆市市级项目(2014-GX-063)
作者简介:岳守春(1978-),男,硕士,讲师,主要从事云模型及云计算研究。
参考文献:
[1] 孙文力, 孙文强. 船载自动识别系统[M]. 大连:大连海事大学出版社, 2004.
[2] 李春霞. 卡尔曼滤波方程的改进算法及应用. 哈尔滨商业大学学报(自然科学版), 2002, 18(3), 264-267.
[3] 李德毅. 隶属云和语言原子模型[C]. 计算机智能接口与应用论文集, 1993:272-277.