An Improved Robust Kalman Filter for Real-Time Detection of Cycle Slips in the Single-Frequency Carrier Phase Measurements Validated with BDS Data

  • Ye TianEmail author
  • Yizhe Jia
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


The detection of cycle slips and outliers in single-frequency carrier phase data, or any other type of un-expected changes in the single-frequency carrier phase measurements of the GNSS, is one of the major data preprocessing problems that needs to be addressed, especially single-frequency receivers account for most of the market share and GNSS carrier phase data are used for real-time applications that require reliable position results. In this contribution the improved RKF (Robust Kalman Filter) is designed to detect cycle slips or unexpected changes when single-frequency carrier phase is interfered by small outliers, in order to improve the cycle slip detection success rates. Real BDS single-frequency data have been used to test and evaluate the algorithm, where the simulation results indicate that the improved RKF has a higher cycle slip detection success rate than the RKF when observations are interfered by small outliers, proving the efficiency of the algorithm proposed in this paper.


Improved RKF Cycle slip detection Small outlier BDS 


  1. 1.
    Ju B, Gu D, Chang X et al. Enhanced cycle slip detection method for dual-frequency BeiDou GEO carrier phase observations. GPS Solutions, 2017; p. 1–12.Google Scholar
  2. 2.
    Zhang X, Zeng Q, He J, Kang C. Improving TurboEdit real-time cycle slip detection by the construction of threshold model. Geomatics Inf Sci Wuhan Univ. 2017;42(3):285–92.Google Scholar
  3. 3.
    Zhou W. Research and realization on theories and methods of precise positioning base on BeiDou navigation satellite system. PLA Information Engineering University, 2013.Google Scholar
  4. 4.
    Wang C, Wang J. Cycle slip detection and correction of single frequency undifferenced phase observation. J Tongji Univ (Nat Sci). 2012;40(9):1393–8.Google Scholar
  5. 5.
    Li X, Wu M, Zhang K. Applying outlier test theory to detect and correct cycle-slip. Chin J Sci Instrum. 2012;33(10):2315–21.MathSciNetGoogle Scholar
  6. 6.
    Yao Y, Gao J, Li Z, Tan X. Research on robust kalman filter of observations with unequal precision in precise point positioning. Geomatics Inf Sci Wuhan Univ. 2017;42(3):314–20.Google Scholar
  7. 7.
    Zhang X, Pan Y, Zuo X, Wang J. An improved robust kalman filtering and its application in PPP. Geomatics Inf Sci Wuhan Univ. 2015;40(7):858–64.Google Scholar
  8. 8.
    Yang Y, He H. Adaptive robust filtering for kinematic GPS positioning. Acta Geodaetica Et Cartographica Sinica. 2001;30(4):293–8.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Academy of Space Electrical Information TechnologyXi’anChina

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