Distributed Set-Membership Fusion Estimation for Complex Networks With Communication Constraints
Changzhen Hu, Xiangpeng Xie, Sanbo Ding, Yanhui Jing
Abstract
This paper concerns the distributed set-membership fusion estimation (SMFE) problem of complex networks subject to communication constraints and unknown-but-bounded (UBB) noises, where nodes communicate with their neighbours based on a given topology. There are three main contributions: 1) an event-based coding-decoding mechanism (CDM) is designed in each communication channel to code the transmitted data with a finite bit rate, where the purpose is to save communication resources and enhance transmission security; 2) a fusion estimation method, which can fusion a group of local estimation sets, is introduced to estimate the system state with a trace-maximal ellipsoid, where the local estimators make full use of the information from itself as well as its neighbours. It helps to improve the accuracy and precision of the estimation effectively; 3) a genetic algorithm (GA) is firstly adopted to tackle the co-design issue of bit rate allocation protocol and estimator gain. It aims to reduce decoding errors while ensuring good estimation performance. To demonstrate the superiority of the above contributions, a numerical simulation is provided to validate the proposed SMFE method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Complex networks can be applied to various practical problems, such as biological networks, neural networks, and social networks. The set-membership estimation (SME) method limits the state of the system to an ellipsoidal region. This paper studies the SMFE method, which fuses an ellipsoidal set containing the intersection of all local ellipsoidal sets to form a relatively small region containing the system state, thereby improving the estimation performance. Each communication channel equals with event-based CDM, which improves the robustness and security of data transmission. The bit rate allocation mechanism optimized by GA breaks the traditional uniform allocation scheme, which can reduce decoding error and improve estimation accuracy. Therefore, the method proposed in this paper can be used for navigation and positioning devices.