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Strong Tracking Tobit Kalman Filter with Model Uncertainties

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  • Control Theory and Applications
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Abstract

The Tobit Kalman filter (TKF) is a good choice for applications of discrete time-varying systems with censored measurements. The traditional TKF is usually designed under the assumption of exactly knowing system state and measurement functions, which can not guarantee the good performance and is even unsuitable for system with model mismatch. To ensure the performance, this paper proposes a novel TKF algorithm in the presence of both censored measurements and model uncertainties. Our proposed algorithm, called strong tracking TKF (STTKF), adopts the orthogonal principle as an additional criterion to overcome model mismatch problem. By introducing fading factor into a priori error covariance, STTKF adaptively adjust gain matrix according to different model mismatch degree. Firstly the recursive fading factor formulation is deduced based on orthogonal principle. Then the designed STTKF process is given in a recursive manner. Finally the effectiveness of STTKF is verified by two examples of oscillator system and unmanned aerial vehicle (UAV) transmitter.

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Authors and Affiliations

  1. Baicheng Test Center, Baicheng, 137001, China

    Zhan-long Du

  2. College of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Xiao-min Li

Authors
  1. Zhan-long Du
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  2. Xiao-min Li
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Corresponding author

Correspondence to Zhan-long Du.

Additional information

Recommended by Associate Editor SangKyung Sung under the direction of Editor Duk-Sun Shim.

Zhan-long Du received his B.S. in Communication Engineering in 2009 from Xidian University, Xian, an M.S. in Communication and Information System in 2011 and a Ph.D. in Control Science and Engineering in 2015 from Mechanical Engineering College, Shijiazhuang, China. His current research interests include Kalman filter.

Xiao-min Li is currently a professor in Shijiazhuang Tiedao University, Shijiazhuang, China. His current research focuses on fault detection, navigation theory and virtual training.

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Du, Zl., Li, Xm. Strong Tracking Tobit Kalman Filter with Model Uncertainties. Int. J. Control Autom. Syst. 17, 345–355 (2019). https://doi.org/10.1007/s12555-017-0655-4

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  • DOI: https://doi.org/10.1007/s12555-017-0655-4

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