Hybrid Consensus-Based Cubature Kalman Filtering for Distributed State Estimation in Sensor Networks
Hybrid Consensus-Based Cubature Kalman Filtering for Distributed State Estimation in Sensor Networks
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DOI：
10.1109/jsen.2018.2823908
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发表时间：
2018-06
影响因子：
4.3
通讯作者：
Qian Chen;Chao Yin;Jun Zhou;Yi Wang;Xiangyu Wang;Congyan Chen
中科院分区：
文献类型：
--
作者：
Qian Chen;Chao Yin;Jun Zhou;Yi Wang;Xiangyu Wang;Congyan Chen
本文在容积卡尔曼滤波（CKF）框架下研究了一类传感器网络的高维分布式状态估计问题。该网络由两种类型的节点组成，即，通信和传感器。首先，一个混合的共识为基础的容积卡尔曼滤波（HCCKF）的混合现有的方法，即共识的测量（CM）和共识的信息（CI）。结果表明，该滤波算法具有CM和CI的互补特性，是分布式状态估计问题的较好解决方案。其次，证明了HCCKF的估计误差均方指数有界。最后，在一个传感器网络的目标跟踪案例研究，以证明所提出的HCCKF的有效性。
In this paper, the high-dimensional distributed state estimation problem is investigated for a class of sensor networks within the cubature Kalman filtering (CKF) framework. The network consists of two types of nodes, i.e., communication ones and sensor ones. First, a hybrid consensus-based cubature Kalman filtering (HCCKF) is developed by blending the two existing approaches, namely, consensus on measurements (CM) and consensus on information (CI). As a result, the proposed filtering algorithm has complementary features of CM and CI, which turns out to be a better solution to the distributed state estimation problem. Secondly, estimation errors in HCCKF are proved to be exponentially bounded in mean square. Finally, a target tracking case-study in an example sensor network is given to demonstrate the effectiveness of the proposed HCCKF.