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Unscented recursive three-step filter based unbiased minimum-variance estimation for a class of nonlinear systems

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  • Yike Zhang
  • Guoming Liu
  • Xinmin Song

Abstract

For the direct feedthrough system where unknown input affects both process equation and measurement equation, an unscented recursive three-step filter based unbiased minimum-variance (URTSF-UMV) estimation algorithm is proposed. The algorithm is based on the unbiased minimum-variance (UMV) method and uses statistical linearisation technology to approximate the nonlinear system and the nonlinear measurement function to linear regression form. The estimation of unknown input is obtained from innovation through weighted least-squares (WLS) estimation. The approximate linear regression form allows us to process the nonlinear system with unknown input direct feedthrough by using the UMV state estimation framework, and derive the filter by minimising the trace of the state error covariance of the measurement update under the unbiased condition. The URTSF-UMV requires that the unknown input distribution matrix is full column rank. We use the full rank decomposition method to deal with the nonlinear system containing the unknown input rank-deficient distribution matrix. The effectiveness of the URTSF-UMV algorithm is demonstrated through an illustrative simulation example.

Suggested Citation

  • Yike Zhang & Guoming Liu & Xinmin Song, 2025. "Unscented recursive three-step filter based unbiased minimum-variance estimation for a class of nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 56(2), pages 227-236, January.
    • Handle: RePEc:taf:tsysxx:v:56:y:2025:i:2:p:227-236
    DOI: 10.1080/00207721.2024.2392817
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