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首页> 外文期刊>International Journal of Innovative Computing Information and Control >RECEDING HORIZON FILTERING FOR MULTISENSOR LINEAR DYNAMICS SYSTEMS
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RECEDING HORIZON FILTERING FOR MULTISENSOR LINEAR DYNAMICS SYSTEMS

机译：多传感器线性动力学系统的水平滤波

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摘要

Distributed receding horizon discrete-time filtering is presented here, which combines a Kalman filter and receding horizon strategy. Distributed fusion with the weighted sum structure is then applied to local receding horizon Kalman filters (LRHKFs) having non-equal horizon time intervals. The proposed distributed algorithm has a parallel structure that allows for the parallel processing of observations, thereby making it more reliable than the centralized version if some sensors become faulty. Moreover, the choice of receding horizon strategy makes the proposed algorithm robust against dynamic model uncertainties. Note that the derivation of the error cross-covariances between the LRHKFs is the key contribution in this distributed algorithm. The subsequent application of the proposed distributed filter to linear discrete-time dynamic systems within a multisensor environment demonstrates and confirms its effectiveness.
机译：本文介绍了分布式后备地平线离散时间滤波，它结合了卡尔曼滤波器和后备地平线策略。然后将具有加权和结构的分布式融合应用于具有不相等的地平线时间间隔的局部后退地平线卡尔曼滤波器（LRHKF）。所提出的分布式算法具有并行结构，该并行结构允许对观测值进行并行处理，从而在某些传感器出现故障时使其比集中式版本更可靠。此外，后退视野策略的选择使所提出的算法对动态模型不确定性具有鲁棒性。请注意，LRHKF之间的误差互协方差的推导是此分布式算法的关键作用。所提出的分布式滤波器在多传感器环境中线性离散时间动态系统的后续应用证明并证实了其有效性。

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