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首页> 外文期刊>Aerospace science and technology >Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties
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Robust integrated covariance intersection fusion Kalman estimators for networked systems with a unified measurement model including five uncertainties

机译：具有统一测量模型的网络系统的强大集成协方差交叉融合融合器，包括五种不确定性

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摘要

For networked multisensor systems (NMSs), a soft measurement model with five uncertainties is presented, which can be viewed as a "soft sensor" such that the measurements received by estimators can be obtained via the computations of this model. A novel "soft sensor" concept is presented. Three new approaches are presented, which include a fictitious white noises approach with compensating random uncertainties, an extended Lyapunov equation approach with three kinds of the Lyapunov equations, and a universal integrated covariance intersection (ICI) fusion approach with integrating local estimators and their cross-covariances. Applying them, the universal ICI and two fast ICI (FICI) fusion time-varying and steady-state minimax robust Kalman estimators (predictor, filter and smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. They improve the robust accuracies of the original covariance intersection (CI) and fast CI(FCI) fusers, and overcome their drawback to require known local estimators and their conservative variances. Their robustness, accuracy relations, stability, steady-state property, and three modes of convergence are proved. The proposed new concept, approaches and results constitute a new methodology, a universal robust fusion Kalman filtering theory and a new filtering stability theory. A simulation example applied to the vehicle suspension system shows their effectiveness and applicability. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：对于网络多传感器系统（NMS），提出了具有五个不确定性的软测量模型，其可以被视为“软传感器”，使得可以通过该模型的计算获得由估计器接收的测量。提出了一种新颖的“软传感器”概念。提出了三种新方法，包括一种具有补偿随机不确定性的虚拟的白色噪声方法，具有三种Lyapunov方程的扩展Lyapunov方程方法，以及具有集成本地估算器及其交叉的通用集成协方识（ICI）融合方法。共同途径。应用它们，通用ICI和两种快速ICI（FICI）融合时变速和稳态MIMIMAX强大的卡尔曼估计（预测器，过滤器和更顺畅）都是如此，因为它们的实际估计误差方差得到了相应的最小值上限。它们提高了原始协方差交叉口（CI）和快速CI（FCI）定影的稳健准确性，并克服了他们要求已知的本地估算器及其保守差异的缺点。其鲁棒性，准确性，稳定性，稳态性质和三种收敛方式。提出的新概念，方法和结果构成了一种新的方法，一种普遍的强大融合卡尔曼滤波理论和新的过滤稳定理论。应用于车辆悬架系统的仿真示例显示了它们的有效性和适用性。 （c）2021 Elsevier Masson SAS。版权所有。

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