存在自回归输入的鲁棒两阶段卡尔曼滤波

Huiping Zhuang, Junhui Li
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引用次数: 2

摘要

提出了两级卡尔曼滤波器,目的是避免系统在输入信号存在时产生偏置。该技术的一个常见技术难点是输入信号的动态总是未知的,而这种滤波器的最优性只能通过足够的先验知识(即已知的动态和统计)来实现。无偏最小方差滤波器即使在存在未知输入的情况下也能获得无偏状态估计,但代价是它失去了获得更准确状态估计的能力。本文利用这两种估计器的优点,提出了一种结合它们优点的新估计器,并讨论了输入信号显示自回归特性时的估计问题。我们设法在参数识别过程之前同时从无偏最小方差滤波器估计输入信号,这对于获取输入动态所需的信息具有重要意义。本文还证明了输入估计器的无偏性。然后通过将其转换为求解特征向量问题来完成识别步骤。该滤波器在无偏最小方差滤波器和两阶段卡尔曼滤波器之间架起了一座桥梁,仿真结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust two-stage Kalman filtering in presence of autoregressive input
The two-stage Kalman filter was proposed with the objective to avoid bias when the system is in presence of input signals. A common technical difficulty in this technique is that the dynamics of input signal is always unknown whereas the optimality of such filter can only be achieved with sufficient priori knowledge (i.e., known dynamics and statistics). Unbiased minimum-variance filter is capable of obtaining unbiased state estimates even in presence of an unknown input but the price is paid and it loses the capability to gain access to more accurate state estimates. This paper takes advantages of both estimators to propose a new estimator that combines their merits and discuss an estimation problem when the input signal displays autoregressive property. We manage to simultaneously estimate the input signal from unbiased minimum-variance filter ahead of the parameter identification procedure, which is of significance as the information is required to procure input dynamics. The un-biasedness of the input estimator is also proved in the paper. The identification step is then completed by converting it into solving an eigenvector problem. This proposed filter builds a bridge connecting unbiased minimum-variance filter and two-stage Kalman filter and the validity of the proposed method is justified via simulation results.
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