Abstract
A robust Kalman filter based on Chi square test with sequential measurement update is proposed. This approach can not only handle outliers in part or even individual measurement channel, but can also further improve the accuracy especially when a novel ordering strategy in processing the measurement elements is adopted. The accuracy improvement can be attributed to the higher statistical efficiency, i.e., an increased probability of correctly resisting the outlying measurement elements and retaining the good ones. The accuracy improvement of the proposed method is illustrated by a simulating example.
Similar content being viewed by others
References
Baarda W (1968) A testing procedure for use in geodetic networks. Publ Geod New Ser 2(5):1–97
Bogatin S, Kogoj D (2008) Processing kinematic geodetic measurements using Kalman filtering. Acta Geodaetica et Geophysica Hungarica 43(1):53–74
Chang G (2014) Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion. J Geod 88(4):391–401
Chang G, Liu M (2015) M-estimator based robust Kalman filter for systems with process modeling errors and rank deficient measurement models. Nonlin Dyn 80(3):1431–1449
De Jong K (2000) Minimal detectable biases of cross-correlated GPS observations. GPS Solut 3(3):12–18
Guo J (2013) The case-deletion and mean-shift outlier models: equivalence and beyond. Acta Geodaetica et Geophysica 48(2):191–197
Hekimoğlu S, Erdogan B, Erenoglu R, Hosbas R (2011) Increasing the efficacy of the tests for outliers for geodetic networks. Acta Geodaetica et Geophysica Hungarica 46(3):291–308
Huber PJ (1964) Robust estimation of a location parameter. Ann Math Stat 35(1):73–101
Huber PJ, Ronchetti EM (2009) Robust statistics. Wiley, New Jersey
Kailath T, Sayed AH, Hassibi B (2000) Linear estimation. Prentice Hall, New Jersey
Karlgaard CD (2015) Nonlinear regression huber-kalman filtering and fixed-interval smoothing. J Guid Control Dyn 38(2):322–330
Koch KR (2015) Minimal detectable outliers as measures of reliability. J Geod 89(5):483–490
Koch KR, Yang Y (1998) Robust Kalman filter for rank deficient observation models. J Geod 72(7):436–441
Lehmann R (2012) Improved critical values for extreme normalized and studentized residuals in Gauss–Markov models. J Geod 86:1137–1146
Lehmann R (2013a) 3σ-Rule for outlier detection from the viewpoint of geodetic adjustment. J Surv Eng 139(4):157–165
Lehmann R (2013b) On the formulation of the alternative hypothesis for geodetic outlier detection. J Geod 87:373–386
Perfetti N (2006) Detection of station coordinate discontinuities within the Italian GPS fiducial network. J Geod 80(7):381–396
Pope A. (1976). The statistics of residuals and the detection of outliers. NOAA Technical Report, NOS 65(NGS 1): 1-133
Salzmann M (1991) MDB: a design tool for integrated navigation systems. Bull Géod 65(2):109–115
Simon D (2006) Optimal state estimation: Kalman, H∞, and nonlinear approaches. Wiley, New Jersey
Teunissen PJG. (1990a). An integrity and quality control procedure for use in multi sensor integration. In: Proceedings ION GPS, pp 19–21
Teunissen PJG (1990b) Quality control in integrated navigation systems. IEEE Aerosp Electron Syst Mag 5(7):35–41
Teunissen PJG (1998) Minimal detectable biases of GPS data. J Geod 72(4):236–244
Teunissen PJG, De Bakker PF (2013) Single-receiver single-channel multi-frequency GNSS integrity: outliers, slips, and ionospheric disturbances. J Geod 87(2):161–177
Teunissen PJG, Salzmann MA (1989) A recursive slippage test for use in state-space filtering. Manuscripta geodaetica 14(6):383–390
Třasák P, Štroner M (2014) Outlier detection efficiency in the high precision geodetic network adjustment. Acta Geodaetica et Geophysica 49(2):161–175
Yang Y (1991) Robust bayesian estimation. Bull Géod 65(3):145–150
Yang Y, He H, Xu G (2001) Adaptively robust filtering for kinematic geodetic positioning. J Geod 75(2):109–116
Acknowledgments
The authors are grateful to two anonymous reviewers whose valuable comments improved the paper significantly. This work was supported by the National Natural Science Foundation of China (No. 41404001).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chang, G., Wang, Y. & Wang, Q. Accuracy improvement by implementing sequential measurement update in robust Kalman filter. Acta Geod Geophys 51, 421–433 (2016). https://doi.org/10.1007/s40328-015-0134-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40328-015-0134-4