Stability of consensus extended Kalman filter for distributed state estimation
Stability of consensus extended Kalman filter for distributed state estimation
复制标题
分布状态估计的一致扩展卡尔曼滤波的稳定性
DOI：
10.1016/j.automatica.2016.01.071
复制
发表时间：
2016-06-01
期刊：
影响因子：
6.4
通讯作者：
Chisci, Luigi
中科院分区：
文献类型：
--
作者：
Battistelli, Giorgio;Chisci, Luigi
本文研究了基于共识的非线性动力系统状态网络估计。重点研究了一类基于扩展卡尔曼滤波线性化范式的分布式状态估计算法。共识被用来融合每个网络节点中现有的和新的信息。结果表明，所考虑的分布式扩展卡尔曼滤波器族在最小的网络连通性和系统集体可观测性要求下具有局部稳定性。通过一个非线性（角度和距离）位置传感器网络的目标跟踪仿真实例，验证了所考虑的非线性一致滤波器的有效性。(C) 2016 Elsevier Ltd.版权所有。
The paper addresses consensus-based networked estimation of the state of a nonlinear dynamical system. The focus is on a family of distributed state estimation algorithms which relies on the extended Kalman filter linearization paradigm. Consensus is exploited in order to fuse the information, both prior and novel, available in each network node. It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability. A simulation case-study concerning target tracking with a network of nonlinear (angle and range) position sensors is worked out in order to show the effectiveness of the considered nonlinear consensus filter. (C) 2016 Elsevier Ltd. All rights reserved.