BA Xiaohui, LIU Haiyang, ZHENG Rui, CHEN Jie. An Effective Carrier-to-noise Ratio Estimation Method for GNSS Receiver[J]. Geomatics and Information Science of Wuhan University, 2011, 36(4): 457-460.
Citation: BA Xiaohui, LIU Haiyang, ZHENG Rui, CHEN Jie. An Effective Carrier-to-noise Ratio Estimation Method for GNSS Receiver[J]. Geomatics and Information Science of Wuhan University, 2011, 36(4): 457-460.

An Effective Carrier-to-noise Ratio Estimation Method for GNSS Receiver

Funds: 国家863计划资助项目(2009AA011700)
More Information
  • Received Date: March 10, 2011
  • Published Date: April 04, 2011
  • In this paper,we give the mathematical derivation for these estimation methods and discuss their tracking performance in both strong and weak signal environments.Meanwhile,a novel method,I-Branch Power-to-Variance Ratio(IBPVR),for the CNR estimation is proposed in this paper.Experiment results show that the NWPR and SNV methods do not have the saturation phenomenon when the CNR is above 50 dBHz.In weak signal environments,the SNV method has large estimation errors,whereas the NWPR and IBPVR methods have excellent estimation performances.The method IBPVR only uses the signal power in I-Branch to estimate CNR for the GNSS receivers and hence has the minimum calculation complexity among all the studied methods.
  • Related Articles

    [1]LIU Junnan, LIU Haiyan, CHEN Xiaohui, GUO Xuan, GUO Wenyue, ZHU Xinming, ZHAO Qingbo, LI Jia. Terrorism Event Model by Knowledge Graph[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 313-322. DOI: 10.13203/j.whugis20190428
    [2]LU Wei, AI Tinghua. Center Point Extraction of Simple Area Object Using Triangulation Skeleton Graph[J]. Geomatics and Information Science of Wuhan University, 2020, 45(3): 337-343. DOI: 10.13203/j.whugis20180236
    [3]YUAN Xiuxiao, YUAN Wei, CHEN Shiyu. An Automatic Detection Method of Mismatching Points in Remote Sensing Images Based on Graph Theory[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1854-1860. DOI: 10.13203/j.whugis20180154
    [4]WANG Ping, WEI Zheng, CUI Weihong, LIN Zhiyong. A Image Segmentation Method Based on Statistics Learning Theory and Minimum Spanning Tree[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 877-883. DOI: 10.13203/j.whugis20150345
    [5]TIAN Jing, SONG Zihan, AI Tinghua. Grid Pattern Extraction in Road Networks with Graph[J]. Geomatics and Information Science of Wuhan University, 2012, 37(6): 724-727.
    [6]DENG Min, LIU Qiliang, LI Guangqiang, XIAO Qi. A Spatial Clustering Algorithm Based on Minimum Spanning Tree-like[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11): 1360-1364.
    [7]WAN Youchuan, HUANG Jun. Influence of Geometric and Graph Theoretical Measures on Land Classification Using High-Resolution Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7): 794-798.
    [8]XIA Lanfang, HU Peng, HUANG Menglong. Zero Initialization of Spatial Data and Minimum Spanning Tree Algorithm in Presence of Arbitrary Obstacles[J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 60-63.
    [9]ZHANG Yuanyu, LI Lin, JIN Yuping, ZHU Haihong. Structured Design of Dendritic River Networks Based on Graph[J]. Geomatics and Information Science of Wuhan University, 2004, 29(6): 537-539,543.
    [10]Lin Zongjian, Fu Zhongliang. Automatic Separation of Graph/Symbol in Topographic Map[J]. Geomatics and Information Science of Wuhan University, 1994, 19(4): 328-331.

Catalog

    Article views (1661) PDF downloads (594) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return