Probability-Guaranteed Distributed Filtering for Nonlinear Systems on Basis of Nonuniform Samplings Subject to Envelope Constraints

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-11-15 DOI:10.1109/TSIPN.2024.3496254

Wei Wang;Chen Hu;Lifeng Ma;Xiaojian Yi

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引用次数: 0

Abstract

This paper investigates the probability-guaranteed distributed

$H_\infty$

filtering problem for stochastic time-varying systems over sensor networks. The measurements from sensing nodes are sampled nonuniformly before being received by filters and the sampling processes are modeled by a set of Markov chains. The purpose of the addressed problem is to design a distributed filter algorithm which meets the finite-horizon average

$H_\infty$

performance, meanwhile guaranteeing all filtering errors bounded within a prespecified envelope with a certain probability. Sufficient conditions for the feasibility of the mentioned filtering technique are established using convex optimization techniques. The desired filtering gains are subsequently determined by resolving the relevant matrix inequalities at each time step. Finally, the effectiveness of the proposed filtering algorithm is shown via an illustrative numerical example.

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基于包络约束的非均匀采样的非线性系统概率保证分布式滤波

本文研究了传感器网络上随机时变系统的概率保证分布式 $H_\infty$ 滤波问题。在滤波器接收来自传感节点的测量值之前，会对其进行非均匀采样，采样过程由一组马尔可夫链建模。该问题的目的是设计一种分布式滤波算法，该算法既要满足有限视距平均 $H_\infty$ 性能，又要保证所有滤波误差以一定概率约束在一个预先指定的包络内。利用凸优化技术建立了上述过滤技术可行性的充分条件。随后，通过解决每个时间步长的相关矩阵不等式，确定了所需的滤波增益。最后，通过一个数值示例说明了所提出的滤波算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.