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首页> 外文期刊>Journal of applied mathematics >Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances
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Weighted Fusion Robust Steady-State Kalman Filters for Multisensor System with Uncertain Noise Variances

机译：具有不确定噪声方差的多传感器系统的加权融合鲁棒稳态稳态卡尔曼滤波器

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摘要

A direct approach of designing weighted fusion robust steady-state Kalman filters with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule, the six robust weighted fusion steady-state Kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. The actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. A Lyapunov equation method for robustness analysis is proposed. Their robust accuracy relations are proved. A simulation example verifies their robustness and accuracy relations.
机译：提出了一种设计具有不确定噪声方差的加权融合鲁棒稳态卡尔曼滤波器的直接方法。基于稳态卡尔曼滤波理论，利用最小极大鲁棒估计原理和无偏线性最小方差（ULMV）最优估计规则，基于最坏情况保守系统，设计了六个鲁棒加权融合稳态卡尔曼滤波器。噪声方差的保守上限。对于所有允许的噪声方差不确定性，每个定影器的实际滤波误差方差均保证具有最小上限。提出了一种用于鲁棒性分析的李雅普诺夫方程方法。证明了其鲁棒的精度关系。仿真示例验证了它们的鲁棒性和准确性关系。

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