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Minimum Variance Estimator Design for Scalar Quadratic Maps

机译:标量二次图的最小方差估计器设计

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摘要

In this paper, we consider the state estimation problem for scalar discrete-time nonlinear systems with second degree polynomial nonlinearities. A novel state estimator is proposed where the Extended Kalman filter structure is generalized to include quadratic terms and two consecutive measurements. The unbiased minimum variance estimator for the given structure is derived. It is shown both mathematically and by simulations that the new estimator achieves lower mean square estimation error than the Extended Kaiman filter. It is also shown in simulations that the new estimator fares well in performance with the recently developed current output filter.
机译:本文考虑具有二阶多项式非线性的标量离散时间非线性系统的状态估计问题。提出了一种新颖的状态估计器,其中扩展卡尔曼滤波器结构被概括为包括二次项和两个连续的测量值。得出给定结构的无偏最小方差估计量。数学和仿真均表明,新的估计器比扩展的Kaiman滤波器实现了更低的均方根估计误差。在仿真中还显示,新的估算器与最近开发的电流输出滤波器相比,性能良好。

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