RAIM Algorithm Based on Residual Separation

被引:0
|
作者
Li, Zhaoyang [1 ]
Li, Qingsong [1 ]
Wu, Jie [1 ]
机构
[1] Natl Univ Def Technol, Coll Aerosp Sci & Engn, Changsha 410073, Hunan, Peoples R China
关键词
Residual-based separation; Fault detection; Fault exclusion; RAIM;
D O I
10.1007/978-981-10-4591-2_19
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
With the rapid development of Global Navigation Satellite Systems (GPS, BDS, GALILEO and GLONASS), the number of available satellites is increasing. The redundancy of GNSS is enhanced. This will offer the possibility of realizing receiver fault detection and exclusion (FDE). Due to factors such as satellite fault and strong electromagnetic interference, measurements from some satellite are vulnerable to become outliers. Unacceptable positioning errorswill occur unless such faultymeasurements are detected and excluded. Therefore, it is very important to apply RAIM (Receiver Automatic Integrity Monitoring) algorithm for improving the positioning precision. The main function of RAIM algorithm includes two aspects: fault detection and fault exclusion. This paper presents a RAIM algorithm based on residual separation. The algorithm uses the residual sum of squares of observation equations as test statistics. When test statistics exceed alarm threshold, it can be considered that there are faults in measurements. And then the visible satellites are excluded at every turn, while multi-combinations of remaining measurements are obtained. After SSE (Sum of Squared Error) calculated, the combination of remaining measurements with smallest SSE is the optimal result. Field test data and simulation data are processed in this work. The RAIM algorithm based on residual separation can not only avoid excluding too many satellites, but also improve the positioning accuracy in the case of containing faults.
引用
收藏
页码:233 / 243
页数:11
相关论文
共 50 条
  • [1] Solution Separation Versus Residual-Based RAIM
    Joerger, Mathieu
    Chan, Fang-Cheng
    Pervan, Boris
    [J]. NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION, 2014, 61 (04): : 273 - 291
  • [2] An enhanced least squares residual RAIM algorithm based on optimal decentralized factor
    Sun, Guanghui
    Xu, Chengdong
    Song, Dan
    Jian, Yimei
    [J]. CHINESE JOURNAL OF AERONAUTICS, 2020, 33 (12) : 3369 - 3379
  • [3] RAIM Algorithm Based on Robust Extended Kalman Particle Filter and Smoothed Residual
    Li, Zhen
    Song, Dan
    Niu, Fei
    Xu, Chengdong
    [J]. CHINA SATELLITE NAVIGATION CONFERENCE (CSNC) 2017 PROCEEDINGS, VOL II, 2017, 438 : 209 - 220
  • [4] An enhanced least squares residual RAIM algorithm based on optimal decentralized factor
    Guanghui SUN
    Chengdong XU
    Dan SONG
    Yimei JIAN
    [J]. Chinese Journal of Aeronautics, 2020, 33 (12) : 3369 - 3379
  • [5] An enhanced least squares residual RAIM algorithm based on optimal decentralized factor
    Guanghui SUN
    Chengdong XU
    Dan SONG
    Yimei JIAN
    [J]. Chinese Journal of Aeronautics, 2020, (12) - 3379
  • [6] Sequential Residual-Based RAIM
    Joerger, Mathieu
    Pervan, Boris
    [J]. PROCEEDINGS OF THE 23RD INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2010), 2010, : 3167 - 3180
  • [7] A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
    Wang, Wenbo
    Xu, Ying
    [J]. SENSORS, 2020, 20 (18) : 1 - 19
  • [8] Correlation-weighted least squares residual algorithm for RAIM
    Song, Dan
    Shi, Chuang
    Wang, Zhipeng
    Wang, Cheng
    Jing, Guifei
    [J]. CHINESE JOURNAL OF AERONAUTICS, 2020, 33 (05) : 1505 - 1516
  • [9] Correlation-weighted least squares residual algorithm for RAIM
    Dan SONG
    Chuang SHI
    Zhipeng WANG
    Cheng WANG
    Guifei JING
    [J]. Chinese Journal of Aeronautics., 2020, 33 (05) - 1516
  • [10] Correlation-weighted least squares residual algorithm for RAIM
    Dan SONG
    Chuang SHI
    Zhipeng WANG
    Cheng WANG
    Guifei JING
    [J]. Chinese Journal of Aeronautics, 2020, (05) : 1505 - 1516