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Guaranteed cost robust weighted measurement fusion steady-state Kalman predictors with uncertain noise variances

机译：具有不确定噪声方差的保证成本的鲁棒加权测量融合稳态卡尔曼预测器

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摘要

Two classes of guaranteed cost robust weighted measurement fusion (WMF) one-step and multi-step Kalman predictors are presented by the Lyapunov equation approach for multisensor system with uncertain noise variances based on the minimax robust estimation principle. One class is to construct a maximal perturbation region of uncertain noise variances such that for all admissible perturbations in this region, the deviations of its actual accuracies with respect to the robust accuracy are guaranteed to remain within the prescribed range, and the maximal lower bound and minimal upper bound of accuracy deviations are given. The other class is to find minimal upper bound and maximal lower bound of accuracy deviations under given perturbation region of uncertain noise variances. The general and unified concept of guaranteed cost robustness is presented. The proof of the guaranteed cost robustness is presented by the Lyapunov equation approach. A simulation example shows the correctness and effectiveness of the proposed results. (C) 2015 Elsevier Masson SAS. All rights reserved.
机译：基于最小极大鲁棒估计原理，针对具有不确定噪声方差的多传感器系统，利用Lyapunov方程方法，提出了两类保证成本的鲁棒加权测量融合（WMF）一步和多步卡尔曼预测器。一类是构造一个不确定的噪声方差的最大扰动区域，以便对于该区域中的所有容许扰动，保证其实际精度相对于鲁棒精度的偏差都保持在规定的范围内，并且最大下限和给出了精度偏差的最小上限。另一类是在不确定的噪声方差的给定扰动区域下找到精度偏差的最小上限和最大下限。提出了保证成本稳健性的通用概念。 Lyapunov方程方法提供了保证的成本稳健性的证明。仿真算例表明了所提出结果的正确性和有效性。 （C）2015 Elsevier Masson SAS。版权所有。

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