Circuits, Systems, and Signal Processing

, Volume 35, Issue 5, pp 1767–1782 | Cite as

Design of Sigma-Point Kalman Filter with Recursive Updated Measurement

  • Yulong Huang
  • Yonggang ZhangEmail author
  • Ning Li
  • Lin Zhao
Short Paper


In this study, the authors focus on improving measurement update of existing nonlinear Kalman approximation filter and propose a new sigma-point Kalman filter with recursive measurement update. Statistical linearization technique based on sigma transformation is utilized in the proposed filter to linearize the nonlinear measurement function, and linear measurement update is applied gradually and repeatedly based on the statistically linearized measurement equation. The total measurement update of the proposed filter is nonlinear, and the proposed filter can extract state information from nonlinear measurement better than existing nonlinear filters. Simulation results show that the proposed method has higher estimation accuracy than existing methods.


Nonlinear filtering Recursive measurement update Sigma-point Kalman filter Iterated Kalman filter Progressive Gaussian filtering Statistical linearization 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yulong Huang
    • 1
  • Yonggang Zhang
    • 1
    Email author
  • Ning Li
    • 1
  • Lin Zhao
    • 1
  1. 1.College of AutomationHarbin Engineering UniversityHarbinPeople’s Republic of China

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