LIU Yi, GU Shouzhou, BIAN Shaofeng, BEI Jinzhong, CUI Congcong. A New RAIM Algorithm Based on the Density Center of Observed Dataset[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1900-1906. DOI: 10.13203/j.whugis20210234
Citation: LIU Yi, GU Shouzhou, BIAN Shaofeng, BEI Jinzhong, CUI Congcong. A New RAIM Algorithm Based on the Density Center of Observed Dataset[J]. Geomatics and Information Science of Wuhan University, 2021, 46(12): 1900-1906. DOI: 10.13203/j.whugis20210234

A New RAIM Algorithm Based on the Density Center of Observed Dataset

Funds: 

The National Key Research and Development Program of China 2016YFB0501801

the National Natural Science Foundation of China 41631072

the National Natural Science Foundation of China 41971416

iGMAS GFZX0301040308-06

the Natural Science Foundation for Distinguished Young Scholars of Hubei Province of China 2019CFA086

the Project Funded by Guangxi Key Laboratory of Spatial Information and Geomatics 19-050-11-02

More Information
  • Author Bio:

    LIU Yi, PhD candidate, majors in GNSS precise point positioning. E-mail: liuyinue@sina.cn

  • Corresponding author:

    GU Shouzhou, PhD, associate researcher. E-mail: gusz@casm.ac.cn

  • Received Date: May 18, 2021
  • Published Date: December 04, 2021
  •   Objectives  With the development of the global navigation satellite system (GNSS), the number of visible satellites has increased, and the constellation configuration has been improved. While improving user observation information, multi-system GNSS positioning increases the risk of multiple gross errors, posing a threat to the integrity of the system and restricting the application of GNSS in complex environments. A common method, the receiver autonomous integrity monitoring (RAIM) to ensure the reliability of positioning based on single error, but the reliability of positioning would decline in the case of multiple gross errors. The problem of poor detection and recognition, based on the correlation analysis method of the post-test residual vector, the residual vector is affected by multiple gross errors, showing that the correlation with the observed feature vector of gross errors is weakened. The phenomenon makes gross error detection distortion. The aim of the study is to improving the accuracy of multiple gross errors detection by a new RAIM algorithm based on the density center of observed dataset.
      Methods  This paper proposes a correlation analysis method based on the density center of the observation data set to realize the detection and identification of multiple gross errors. Firstly, we construct the observation dataset through the QR calibration method. Then, we estimate the density center by the improved Mean Shift model. Finally, we test the correlation distance between the observation feature points and the density center for detection and recognition of multiple gross errors was compared.
      Result  The gross errors are simulated by the factual observation data, and the correlation distance of density center to gross error satellite and normal satellite, In the case of a single gross error, two gross errors, and three gross errors, the average difference correlation distance are 1.122 m and 1.516 m, 1.021 m and 1.266 m, 1.177 m and 1.588 m respectively. Compared the correlation distance of QR test vector to gross error satellite and normal satellite, the average difference correlation distance are 0.639 m and 1.142 m, 0.497 m and 0.510 m, 0.108 m and 0.198 m respectively.
      Conclusions  The new RAIM algorithm overcomes the problem of gross error detection distortion caused by the reduced correlation between the calibration vector and the observation vector in the presence of multiple gross errors, which can effectively improve the reliability of multi-GNSS positioning.
  • [1]
    Hewitson S, Wang J L. GNSS Receiver Autono‐ mous Integrity Monitoring (RAIM) Performance Analysis[J]. GPS Solutions, 2006, 10 (3) : 155-170 doi: 10.1007/s10291-005-0016-2
    [2]
    Yang Y X, Xu J Y. GNSS Receiver Autonomous Integrity Monitoring (RAIM) Algorithm Based on Robust Estimation[J]. Geodesy and Geodynamics, 2016, 7(2): 117-123 doi: 10.1016/j.geog.2016.04.004
    [3]
    祝会忠, 李军, 徐爱功, 等. 灾害应急环境下智能终端高精度北斗增强定位方法[J]. 武汉大学学报·信息科学版, 2020, 45(8): 1155-1167 doi: 10.13203/j.whugis20200123

    Zhu Huizhong, Li Jun, Xu Aigong, et al. High-Pre‐ cision BDS Augmented Positioning Method for Di‐ saster Emergency Environment on Smart Device [J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1 155-1 167 doi: 10.13203/j.whugis20200123
    [4]
    Li P, Jiang X Y, Zhang X H, et al. GPS+Galileo+ BeiDou Precise Point Positioning with Triple ‐ Fre‐ quency Ambiguity Resolution[J]. GPS Solutions, 2020, 24(3): 1-13
    [5]
    赵昂, 杨元喜, 许扬胤, 等. GNSS单系统及多系统组合完好性分析[J]. 武汉大学学报·信息科学版, 2020, 45(1): 72-80 doi: 10.13203/j.whugis20180425

    Zhao Ang, Yang Yuanxi, Xu Yangyin, et al. Integ‐ rity Analysis of GNSS Single System and Multi‐sys‐ tem Combination[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1): 72-80 doi: 10.13203/j.whugis20180425
    [6]
    Song J H, Jee G I. Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Re‐ ceivers in an Urban Area[J]. Sensors, 2016, 16 (10): 1688 doi: 10.3390/s16101688
    [7]
    Titouni S, Rouabah K, Atia S, et al. Spectral Transformation-Based Technique for Reducing Ef‐ fect of Limited Pre-correlation Bandwidth in the GNSS Receiver Filter in Presence of Noise and Mul‐ tipath[J]. Journal of Systems Engineering and Electronics, 2020, 31(2): 252-265 doi: 10.23919/JSEE.2020.000003
    [8]
    Sun J R, Niu Z, Zhu B C. Fault Detection and Ex‐ clusion Method for a Deeply Integrated BDS/INS System[J]. Sensors, 2020, 20(7): 1844 doi: 10.3390/s20071844
    [9]
    Bang E, Milner C, Macabiau C. Cross-Correlation Effect of ARAIM Test Statistic on False Alarm Risk [J]. GPS Solutions, 2020, 24(4): 1-14 doi: 10.1007/s10291-020-00997-w
    [10]
    Li L, W ang H, Jia C, et al. Integrity and Conti‐ nuity Allocation for the RAIM with Multiple Con‐ stellations[J]. GPS Solutions, 2017, 21(4): 1503- 1513 doi: 10.1007/s10291-017-0627-4
    [11]
    Parkinson B W, Axelrad P. Autonomous GPS In‐ tegrity Monitoring Using the Pseudorange Residual [J]. Navigation, 1988, 35(2): 255-274 doi: 10.1002/j.2161-4296.1988.tb00955.x
    [12]
    赵昂, 杨元喜, 许扬胤, 等. 一种使用抗差估计的保护水平重构方法[J]. 武汉大学学报·信息科学版, 2021, 46(1): 96-102 doi: 10.13203/j.whugis20190043

    Zhao Ang, Yang Yuanxi, Xu Yangyin, et al. A Method of Protection Level Reconstruction Based on Robust Estimation[J]. Geomatics and Information Science of Wuhan University, 2021, 46(1): 96-102 doi: 10.13203/j.whugis20190043
    [13]
    Sturza M A. Navigation System Integrity Monitoring Using Redundant Measurements[J]. Navigation, 1988, 35(4): 483-501 doi: 10.1002/j.2161-4296.1988.tb00975.x
    [14]
    Ma X P, Yu K G, Montillet J P, et al. Equivalence Proof and Performance Analysis of Weighted Least Squares Residual Method and Weighted Parity Vec‐ tor Method in RAIM[J]. IEEE Access, 2019, 7: 97803-97814 doi: 10.1109/ACCESS.2019.2929073
    [15]
    Angrisano A, Gaglione S, Crocetto N, et al. PANG-NAV: A Tool for Processing GNSS Mea‐ surements in SPP, Including RAIM Functionality [J]. GPS Solutions, 2019, 24(1): 1-7 doi: 10.1007/s10291-019-0935-y
    [16]
    施闯, 刘经南. 基于相关分析的粗差理论[J]. 武汉测绘科技大学学报, 1998, 23(1): 5-9 doi: 10.3321/j.issn:1671-8860.1998.01.002

    Shi Chuang, Liu Jingnan. Correspondence Based Outlier Analysis[J]. Journal of Wuhan Technical University of Surveying and Mapping, 1998, 23 (1): 5-9 doi: 10.3321/j.issn:1671-8860.1998.01.002
    [17]
    陶本藻, 姚宜斌, 施闯. 基于相关分析的粗差可区分性[J]. 武汉大学学报·信息科学版, 2004, 29(10): 881-884 http://ch.whu.edu.cn/article/id/4486

    Tao Benzao, Yao Yibin, Shi Chuang. Distinguish‐ Ability of Outlier Based on Correlative Analysis[J]. Geomatics and Information Science of Wuhan University, 2004, 29(10): 881-884 http://ch.whu.edu.cn/article/id/4486
    [18]
    秘金钟, 谷守周, 方书山. 基于向量相关距离的新型RAIM算法[J]. 中国科学: 物理学力学天文学, 2010, 40(5): 638-643 https://www.cnki.com.cn/Article/CJFDTOTAL-JGXK201005021.htm

    Bei Jinzhong, Gu Shouzhou, Fang Shushan. A New RAIM Method Based on Vector Correlation Distance[J]. Scientia Sinica(Physica, Mechanica & Astronomica), 2010, 40(5): 638-643 https://www.cnki.com.cn/Article/CJFDTOTAL-JGXK201005021.htm
    [19]
    Gu S Z, Bei J Z, Shi C, et al. RAIM Algorithm Based on Fuzzy Clustering Analysis[J]. Computer Modeling in Engineering & Sciences, 2019, 119 (2): 281-293 http://search.cnki.net/down/default.aspx?filename=CHKD201810001&dbcode=CJFD&year=2018&dflag=pdfdown
    [20]
    Li Z N, Li M, Shi C, et al. A New Fuzzy-ClusterBased Cycle-Slip Detection Method for GPS SingleFrequency Observation[J]. Remote Sensing, 2019, 11(24): 2896 http://www.researchgate.net/publication/337761351_A_New_Fuzzy-Cluster-Based_Cycle-Slip_Detection_Method_for_GPS_Single-Frequency_Observation/download
    [21]
    Guo P C, Liu Z, Wang J J. Radar Group Target Recognition Based on HRRPS and Weighted Mean Shift Clustering[J]. Journal of Systems Engineering and Electronics, 2020, 31(6): 1152-1159 doi: 10.23919/JSEE.2020.000087

Catalog

    Article views PDF downloads Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return