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Distributed state estimation through co-acting Kalman filters
Asian Journal of Control ( IF 2.4 ) Pub Date : 2020-07-15 , DOI: 10.1002/asjc.2358
Subhash Babu Kanagala 1 , Ketan P. Detroja 1
Affiliation  

Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. In this manuscript, a non-iterative way of decomposing the inverse of a matrix is presented. This decomposition allows rewriting the update equations of the Kalman filter subsystem-wise. Subsequently, a Co-acting Kalman Filter (CoKF) is proposed using these decomposed central Kalman filter equations to perform distributed state estimation. The convergence of the CoKF algorithm is established under the assumption that each subsystem is observable. Two variants of the proposed CoKF, namely (m-CoKF and p-CoKF), suitable for applications on opposite ends of computation and communication resource spectrum, are presented along with the trade-offs involved. A comparison of the proposed method with existing distributed Kalman filters is also presented. The proposed CoKF algorithm is implemented on a standard wireless sensor network example with 200 nodes. The simulation results demonstrate the accuracy of the proposed CoKF algorithm relative to the central Kalman filter.

中文翻译:

通过协同卡尔曼滤波器进行分布式状态估计

中央卡尔曼滤波器的分布计算需要卡尔曼滤波器的传播和更新步骤的子系统级表达式。由于更新步骤中存在逆项,很难获得子系统级表达式。在这份手稿中,提出了一种分解矩阵逆的非迭代方法。这种分解允许重写卡尔曼滤波器子系统的更新方程。随后,提出了使用这些分解的中心卡尔曼滤波器方程来执行分布式状态估计的协同卡尔曼滤波器(CoKF)。CoKF算法的收敛性建立在每个子系统都是可观察的假设下。提出的 CoKF 的两个变体,即(m -CoKF 和p-CoKF),适用于计算和通信资源频谱两端的应用,以及所涉及的权衡。还介绍了所提出的方法与现有分布式卡尔曼滤波器的比较。提出的 CoKF 算法是在具有 200 个节点的标准无线传感器网络示例上实现的。仿真结果证明了所提出的 CoKF 算法相对于中央卡尔曼滤波器的准确性。
更新日期:2020-07-15
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