Total Kalman Filter Method of Dynamic EIV Model

doi:10.11947/j.AGCS.2018.20170098

Abstract

Abstract: For the case of the adjustment method of dynamic errors-in-variables(EIV)model ignoring the random errors in the state propagating matrix of system equations,this paper establishes a dynamic EIV model which considers the errors of each elements in both observation equations and system equations.A total Kalman filter method (TKF) and its approximated precision estimator are proposed based on this dynamic EIV model.The similarities and differences of the proposed method,the existing total Kalman filter methods and total least squares (TLS) methods are also analyzed.The results show that the proposed method is statistically superior to the standard Kalman filter method and the existing total Kalman filter methods.

Key words: total least squares, total Kalman filter, dynamic EIV model, prior information, virtual observations

CLC Number:

P207

YU Hang, WANG Jian, WANG Leyang, NING Yipeng, LIU Zhiping. Total Kalman Filter Method of Dynamic EIV Model[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(4): 480-489.

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