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Design of Sigma-Point Kalman Filter with Recursive Updated Measurement

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Abstract

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.

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Authors and Affiliations

  1. College of Automation, Harbin Engineering University, No. 145, Nantong Street, Nangang District, Harbin, 150001, People’s Republic of China

    Yulong Huang, Yonggang Zhang, Ning Li & Lin Zhao

Authors
  1. Yulong Huang
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  2. Yonggang Zhang
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  3. Ning Li
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  4. Lin Zhao
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Corresponding author

Correspondence to Yonggang Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61001154, 61201409 and 61371173, China Postdoctoral Science Foundation Nos. 2013M530147 and 2014T70309, Heilongjiang Postdoctoral Foundation Nos. LBH-Z13052 and LBH-TZ0505, and the Fundamental Research Funds for the Central Universities of Harbin Engineering University No. HEUCFX41307.

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Huang, Y., Zhang, Y., Li, N. et al. Design of Sigma-Point Kalman Filter with Recursive Updated Measurement. Circuits Syst Signal Process 35, 1767–1782 (2016). https://doi.org/10.1007/s00034-015-0137-y

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  • DOI: https://doi.org/10.1007/s00034-015-0137-y

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