[1]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(2):156-161.[doi:10.3969/j.issn.1673-4785.201301020]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
8
Number of periods:
2013 2
Page number:
156-161
Column:
学术论文—智能系统
Public date:
2013-04-25
- Title:
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Data fusion method of vision and SBL position for UUV underwater docking
- Author(s):
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LI Nan1; ZHAN Xin2; CHEN Tao3; YAN Zheping3
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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
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- Keywords:
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unmanned underwater vehicle; underwater docking; vision position; SBL position; data fusion
- CLC:
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TP18;U661.313
- DOI:
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10.3969/j.issn.1673-4785.201301020
- Abstract:
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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.