联邦集成卡尔曼滤波器无复位模式设计

M. Kazerooni, F. Shabaninia, M. Vaziri, S. Vadhva
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引用次数: 6

摘要

本文的主要贡献是设计了一个更精确的最优/次最优容错状态估计器。联邦过滤器由一组本地过滤器和一个主过滤器组成,本地过滤器并行工作,它们的解决方案由主过滤器定期融合,从而产生全局解决方案。针对多传感器数据融合问题,提出了无复位结构的联邦集成卡尔曼滤波器。集成卡尔曼滤波(ENKF)估计被广泛应用于高阶非线性模型、初始状态高度不确定、可获得大量测量值的情况。在联邦滤波器无复位模式设计中,ENKF被用作本地滤波器。将故障检测与隔离(FDI)算法应用于局部滤波器的输出。故障的局部滤波器被主滤波器隔离而不被融合,从而得到容错滤波器。仿真结果验证了所提滤波器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Federated ensemble Kalman filter in no reset mode design
The main contribution of this paper is to design a more accurate optimal/suboptimal fault tolerant state estimator. Federated filters compose of a set of local filters and a master filter, the local filters work in parallel and their solutions are periodically fused by the master filter yielding a global solution. Federated ensemble Kalman filter no reset configuration is developed for multi-sensor data fusion. Ensemble Kalman filter(ENKF) estimation is widely used, where the models are of extremely high order and nonlinear, the initial states are highly uncertain, and a large number of measurements are available. ENKF is used as local filters in federated filter no reset mode design. Fault detection and isolation (FDI) algorithms is applied to local filter's outputs. Faulty local filters are isolated and not fused by master filter to get a fault tolerant filter. Simulation results demonstrate the validity of the proposed filter formation.
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