Volume 40 Issue 8
Aug.  2015
Turn off MathJax
Article Contents
Abstract
References
Related Articles
SHI Chuang, ZHANG Hongping, GU Shengfeng, LOU Yidong, TANG Weiming. Technology of Cloud Positioning and Its Platform for Positioning Service[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 995-999. DOI: 10.13203/j.whugis20150118
Citation: SHI Chuang, ZHANG Hongping, GU Shengfeng, LOU Yidong, TANG Weiming. Technology of Cloud Positioning and Its Platform for Positioning Service[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 995-999. DOI: 10.13203/j.whugis20150118
shu

Technology of Cloud Positioning and Its Platform for Positioning Service

  • 1GNSS Research Center,Wuhan University,Wuhan 430079,China;2State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,;(Wuhan University,Wuhan 430079,China

Funds: The National High Technology Research and Development Program of China(863Program),Nos.2013AA12A204, 2015AA12403.
More Information
  • Author Bio:

    SHI Chuang,PhD,professor,specializes in spatial geodesy and GNSS positioning and navigation.

  • Corresponding author:

    Zhang Hongping,PhD,professor.

  • Received Date: March 06, 2015
  • Revised Date: August 04, 2015
  • Published Date: August 04, 2015
    Graphical Abstract
    • Abstract
  • Traditional centralized high-precision navigation and positioning services cannot meet thepublic users'demand for reliability,scalability and service diversity.This paper describes cloud posi-tioning,and the way in which various positioning resources are managed and integrated comprehen-sively to optimize technology convergence and resources sharing,based on cloud technology.We pres-ent the cloud positioning,framework and discuss the realization of several traditional positioning tech-nologies through the cloud positioning platform,i.e.,GNSS Network RTK,GNSS wide area precisepointing positioning,Wi-Fi positioning,communication base-station positioning and others.We arguethat,compared with the traditional technologies,cloud positioning has more advantages in scalability,reliability,the maintenance cost and feasibility.Using the cloud positioning platform,not only canusers enjoy various high precision positioning services,but also optimizes allocation of resources toprovide personalized services.An efficient business model and technical approach will allow for thepopularization of precise navigation and positioning services.
    • FullText(HTML)
    • References (1)
  • [1]
    Yang Yuanxi.Progress,Contribution and Challen-ges of Compass/Beidou Satellite Navigation System [J].Acta Geoda etica et Cartographica Sinica,2010,39(1):1-6(杨元喜.北斗卫星导航系统的进展、贡献与挑战[J].测绘学报,2010,39(1):1-6)[2] Bookamp H.Global GPS Reference Frame Solu-tions of Unlimited Size[J].Adv Space Res,2010,46(2):136-143[3] Shi Chuang,Lou Yidong,Song Weiwei,et al.A Wide Area Real-time Differential GPS Prototype2007,4:16-25System and the Initial Results[J].Geomatics and [6] Chetty M,Buyya R. Weaving ComputationalInformation Science of Wuhan University,2009, Grids:How Analogous are They with Electrical34(11):1 271-1 274(施闯,楼益栋,宋伟伟,等.广 Grids?[J].Computing in Science &Engineering,域实时精密定位原型系统及初步结果[J].武汉大学 2002,4:61-71学报·信息科学版,2009,34(11):1 271-1 274)[7] Buyya R,Yeo C S,Venugopal S.Market-oriented[4] Shi Chuang,Lou Yidong,Song Weiwei,et al.A Cloud Computing:Vision,Hype,and Reality forWide Area Real-Time Differential GPS Prototype Delivering It Services As Computing Utilities[C].System in China and Result Analysis [J]. The 10th IEEE International Conference,Dalian,Surv Rev,2011,43(322):351-3602008[5] Weiss A.Computing in the Clouds[J].Networker,
    • Related Articles

  • Related Articles

    [1]MENG Yiyue, GUO Chi, LIU Jingnan. Deep Reinforcement Learning Visual Target Navigation Method Based on Attention Mechanism and Reward Shaping[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1100-1108. DOI: 10.13203/j.whugis20230193
    [2]GAO Kuiliang, LIU Bing, YU Xuchu, YU Anzhu, SUN Yifan. Automatic Network Structure Search Method for Hyperspectral Image Classification[J]. Geomatics and Information Science of Wuhan University, 2024, 49(2): 225-235. DOI: 10.13203/j.whugis20210380
    [3]WANG Jie, LIU Jiahang, LING Xinpeng, DUAN Zexian. Deep Learning-Based Joint Local and Non-local InSAR Image Phase Filtering Method[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240052
    [4]GUO Congzhou, LI Ke, LI He, TONG Xiaochong, WANG Xiwen. Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292
    [5]LI Yansheng, ZHANG Yongjun. A New Paradigm of Remote Sensing Image Interpretation by Coupling Knowledge Graph and Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1176-1190. DOI: 10.13203/j.whugis20210652
    [6]JI Shunping, LUO Chong, LIU Jin. A Review of Dense Stereo Image Matching Methods Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2021, 46(2): 193-202. DOI: 10.13203/j.whugis20200620
    [7]ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650
    [8]JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132
    [9]PAN Yin, SHAO Zhenfeng, CHENG Tao, HE Wei. Analysis of Urban Waterlogging Influence Based on Deep Learning Model[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 132-138. DOI: 10.13203/j.whugis20170217
    [10]FAN Heng, XU Jun, DENG Yong, XIANG Jinhai. Behavior Recognition of Human Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 492-497. DOI: 10.13203/j.whugis20140110

  • Cited by

    Periodical cited type(5)

    1. 张爱竹，李忍忍，梁树能，孙根云，付航. 联合样本扩充和谱空迭代的高光谱影像分类. 武汉大学学报(信息科学版). 2025(01): 97-109 .
    2. 杜培军，张伟，张鹏，林聪，郭山川，胡泽周. 一种联合空谱特征的高光谱影像分类胶囊网络. 测绘学报. 2023(07): 1090-1104 .
    3. 高奎亮，刘冰，余岸竹，徐佰祺，胡伟，胡家玮. 高光谱影像少样例分类的无监督元学习方法. 测绘学报. 2023(11): 1941-1952 .
    4. 张彬，刘亮，李晓杰，周伟. 基于深度学习的高光谱影像分类方法研究. 红外与毫米波学报. 2023(06): 825-833 .
    5. 孙一帆，余旭初，谭熊，刘冰，高奎亮. 面向小样本高光谱影像分类的轻量化关系网络. 武汉大学学报(信息科学版). 2022(08): 1336-1348 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML
    Article views (2818) PDF downloads (510) Cited by(6)
    Related

    Copyright © 2013 《武汉大学学报·信息科学版》编辑部

    Address：武汉大学文理学部本科生院楼北5楼Post Code：430072

    Tel：027-68778465,68788631E-mail:whuxxb@vip.163.com

    Submit Article

    Submission Guidelines

    e

    Contact Us

    Qrcode

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return