Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies
Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies
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DOI：
10.1109/tcyb.2022.3166649
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发表时间：
2022-05
影响因子：
11.8
通讯作者：
Honghai Ji;Yuzhou Wei;Lingling Fan;Shida Liu;Z. Hou;Li Wang
中科院分区：
文献类型：
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作者：
Honghai Ji;Yuzhou Wei;Lingling Fan;Shida Liu;Z. Hou;Li Wang
针对具有切换拓扑结构的离散时间传感器网络，提出了一种基于一致性协议和信息加权策略的数据驱动分布式过滤方法。通过引入数据驱动的方法，在不需要被控对象模型的情况下，仅利用输入输出数据设计了一个线性状态方程。在辨识阶段，采用数据驱动的自适应优化递推辨识（DD-AORI）来辨识时变参数的递推性。证明了在离散时间切换网络中，执行数据驱动的分布式信息加权一致性滤波（DD-DICF）时，所有节点的估计误差最终有界。该算法结合接收到的邻居和目标节点的直接或间接的观察，以产生修改的增益，从而在一个新的状态估计器包含一个信息交互机制。随后，收敛性分析的基础上的李雅普诺夫方程，以保证DD-DICF估计误差的有界性。仿真结果验证了DD-DICF的性能对理论结果，以及与一些现有的滤波算法进行比较。
This article proposes a data-driven distributed filtering method based on the consensus protocol and information-weighted strategy for discrete-time sensor networks with switching topologies. By introducing a data-driven method, a linear-like state equation is designed by utilizing only the input and output (I/O) data without a controlled object model. In the identification step, data-driven adaptive optimization recursive identification (DD-AORI) is exploited to identify the recurrence of time-varying parameters. It is proved that for discrete-time switching networks, estimation errors of all nodes are ultimately bounded when data-driven distributed information-weighted consensus filtering (DD-DICF) is executed. The algorithm combines with the received neighbors and direct or indirect observations for the target node to produce modified gains, resulting in a novel state estimator containing an information interaction mechanism. Subsequently, convergence analysis is performed on the basis of the Lyapunov equation to guarantee the boundedness of DD-DICF estimate error. Simulations verify the performance of the DD-DICF against the theoretical results as well as in comparison with some existing filtering algorithms.