[1]黎南,湛鑫,陈涛,等.UUV水下回收中的视觉和短基线定位融合[J].智能系统学报,2013,8(02):156-161.[doi:10.3969/j.issn.1673-4785.201301020]
 LI Nan,ZHAN Xin,CHEN Tao,et al.Data fusion method of vision and SBL position for UUV underwater docking[J].CAAI Transactions on Intelligent Systems,2013,8(02):156-161.[doi:10.3969/j.issn.1673-4785.201301020]
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UUV水下回收中的视觉和短基线定位融合(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年02期
页码:
156-161
栏目:
出版日期:
2013-04-25

文章信息/Info

Title:
Data fusion method of vision and SBL position for UUV underwater docking
文章编号:
1673-4785(2013)02-0156-06
作者:
黎南1湛鑫2陈涛3严浙平3
1.海军驻大连地区军代表室,辽宁 大连 116021;
2.中国船舶重工集团公司 第703研究所,黑龙江 哈尔滨 150036;
3.哈尔滨工程大学 自动化学院,黑龙江 哈尔滨 150001
Author(s):
LI Nan1 ZHAN Xin2 CHEN Tao3 YAN Zheping3
1. Military Representative office of PCA Navy in Dalian, Dalian 116021, China;
2. 703 Research Institute of China Shipbuilding Industry Corp.,Haerbin 150004, China;
3. College of Automation, Harbin Engineering University, Haerbin 150001, China
关键词:
UUV水下回收视觉定位短基线定位:数据融合
Keywords:
unmanned underwater vehicle underwater docking vision position SBL position data fusion
分类号:
TP18;U661.313
DOI:
10.3969/j.issn.1673-4785.201301020
文献标志码:
A
摘要:
为了使UUV在水下坞舱回收过程中利用视觉和短基线(short baseline-SBL)进行导引定位,提出了一种视觉和短基线的自适应融合定位方法,以提高导引定位的精度.介绍了短基线定位和视觉定位2种定位系统及其工作原理,以及定位数据的野值剔除和去噪方法.野值剔除采用了一种基于数据变化率的自适应在线野值剔除方法,数据去噪采用了软阈值小波滤波方法.针对传统卡尔曼滤波进行数据融合时先验知识不足的缺点,提出了一种基于模糊逻辑的在线自适应卡尔曼滤波融合方法.通过获取的实时测量数据,实时调整噪声的协方差矩阵来融合2种定位数据.水下回收水池试验结果表明,定位传感器的绝大部分野值被剔除且去噪效果明显,视觉和短基线融合后的定位精度有很大提高,证明了所提方法的有效性.
Abstract:
The vision position and SBL position are applied to underwater docking of UUV, and thus, an adaptive data fusion method for vision and SBL was proposed for position precision improvements. Firstly, SBL, vision position system and their principles were introduced. Next, abnormal value eliminating and denoising methods were described and an adaptive online method based on change rate of data was proposed to eliminate the abnormal value. A soft threshold wavelet filtering method was also proposed for denoising. Taking into consideration the lack of prior knowledge for fusion by using Kalman filter, an adaptive online Kalman filter fusion method based on fuzzy logic was proposed. Covariance matrix of noise was adjusted online for fusion of the two types of position data. Finally, the results of underwater docking in pool tests show that most abnormal values and noise were eliminated remarkably. The results also indicate the position precision was improved by data fusion of vision and SBL position, which prove the proposed method was effective.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-01-16.
网络出版日期:2013-04-09. 
基金项目:国家自然科学基金资助项目(51179038);中央高校基本科研业务费专项资金资助项目(HEUCF041323). 
通信作者:陈涛.
E-mail:chentao_7777@163.com.
作者简介:
黎南,男,1969年生,高级工程师,主要研究方向为船舶自动控制,发表学术论文近10篇.
湛鑫,女,1974年生,副译审,主要研究方向为科技翻译和情报信息处理,发表学术论文3篇.
陈涛,男,1983年生,讲师,主要研究方向为水下无人航行器技术,发表学术论文10余篇.
更新日期/Last Update: 2013-05-26