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A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models

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Abstract

A new algorithm called Mixed L2-Linfty (ML2) estimation is proposed in this paper; it combines both the weighted least squares and the worst-case parameter estimations together as the cost function and strikes the right balance between them. A robust ML2 algorithm and a practical approximate robust ML2 algorithm are also developed under disturbance signals. The properties of the new robust ML2 algorithm are analyzed and the simulation results are given to show the convergence and the validity.

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Authors and Affiliations

  1. Department of Computer Science, University of Houston-Victoria, Victoria, TX, 77901, USA

    Qi Zhu

  2. CGGVeritas, Houston, TX, 77072, USA

    Ying Qiao

  3. Department of Electrical Engineering, National University of Singapore, Singapore, 119260, Singapore

    Shaohua Tan

Authors
  1. Qi Zhu
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  2. Ying Qiao
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  3. Shaohua Tan
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Correspondence to Qi Zhu.

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Zhu, Q., Qiao, Y. & Tan, S. A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models. J Sci Comput 38, 185–206 (2009). https://doi.org/10.1007/s10915-008-9231-7

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  • DOI: https://doi.org/10.1007/s10915-008-9231-7

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