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首页> 外文期刊>Mathematical Problems in Engineering >Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances
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Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances

机译：后视最小二乘估计器在过程和测量噪声协方差估计中的应用

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摘要

This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.
机译：本文提出了一种具有矩形噪声增益矩阵的动力学模型的噪声协方差估计方法。提出了一种在环境不确定性和实验误差下能达到较高估计精度和稳定性的后向视界最小二乘准则。针对所提出标准的优化问题的解决方案给出了一种新型协方差估计器的方程。估算器使用一组具有适当选择的地平线条件的最新信息。特别令人感兴趣的是恒定矩形噪声增益矩阵，其可获得关键的理论结果。它们包括递归形式的后退水平协方差估计器的推导和迭代过程，以选择最佳水平长度。协方差估计器的效率通过其在动态系统上的实现和性能得到证明，该过程具有任意数量的过程和测量噪声。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第15期|5303694.1-5303694.15|共15页
  • 作者

    Shin Vladimir; Thien Rebbecca T. Y.; Kim Yoonsoo;

  • 作者单位

    Gyeongsang Natl Univ Res Inst Nat Sci Dept Informat & Stat Jinju 52828 South Korea;

    Gyeongsang Natl Univ Res Ctr Aircraft Parts Technol Dept Aerosp & Software Engn Jinju 52828 South Korea;

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