Recursive Remote State Estimation for Stochastic Complex Networks With Degraded Measurements and Amplify-and-Forward Relays

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-01-15 DOI:10.1109/TNSE.2025.3528768

Tong-Jian Liu;Zidong Wang;Yang Liu;Rui Wang

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引用次数: 0

Abstract

This paper is concerned with the remote state estimation problem for stochastic complex networks under the effects of degraded measurements and amplify-and-forward (AF) relays. Three sets of random variables are employed to describe the measurement degradation, the sensor transmission energy, and the relay transmission energy, respectively. The measurement from each node is transmitted to an AF relay and then forwarded to the remote estimator to facilitate the state estimation. A novel recursive estimator is constructed in the form of the extended Kalman filter. An upper bound of estimation error covariance is determined by solving Riccati-like difference equations based on the statistical information of the random variables, and such an upper bound is then minimized by choosing an appropriate estimator gain. Furthermore, sufficient conditions are established under which the estimation error is exponentially bounded in the sense of mean square. Finally, the effectiveness of the proposed estimation scheme is demonstrated by some numerical simulations.

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具有退化测量和放大-前向中继的随机复杂网络的递归远程状态估计

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.