Scalable distributed Kalman Filtering for mass-spring systems
Scalable distributed Kalman Filtering for mass-spring systems
复制标题
适用于质量弹簧系统的可扩展分布式卡尔曼滤波
DOI：
10.1109/cdc.2007.4434731
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
A. Rantzer
中科院分区：
文献类型：
--
作者：
T. Henningsson;A. Rantzer
本文研究了质量-弹簧系统的卡尔曼滤波。目标是一个可扩展的分布式实现，其中节点以稀疏模式通信，每个节点的状态估计在本地可用并可用于控制。重点是平移不变系统，利用基于傅里叶变换方法的强大结果。在这种情况下，已知卡尔曼滤波器将具有随距离渐近指数衰减的耦合。示例显示，即使耦合下降得更慢，卡尔曼滤波器增益也可以非常窄地截断，性能损失很小。在分析周期性放置传感器的系统时，向空间变化系统迈出了一步，并表明原始设计对这种空间变化不敏感。
This paper considers Kalman filtering for mass-spring systems. The aim is a scalable distributed implementation where nodes communicate in a sparse pattern and the state estimate for each node is available locally and usable for control. The focus is on translation invariant systems, to make use of the powerful results available based on Fourier transform methods. In this case it is known that Kalman filters will have a coupling that asymptotically falls off exponentially with distance. Examples are shown where the Kalman filter gains can be truncated very narrowly with small performance loss even though the coupling falls off more slowly. A step towards spatially varying systems is taken in analyzing a system with periodically placed sensors, and it is shown that the original design is insensitive to this spatial variation.