Recursive Distributed Filtering for Time-Varying Systems over Sensor Network Via Rayleigh Fading Channels: Tackling Binary Measurements

Fei Han,Zidong Wang,Hongli Dong,Xiaojian Yi,Fuad E. Alsaadi

IEEE Transactions on Signal and Information Processing over Networks（2023）SCI 3区

Northeast Petr Univ

Cited 8|Views30

Abstract

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic systems over binary sensor networks with the Rayleigh fading channels. Both the system state and measurement are subject to random noises with known statistical information, where the distribution function of measurement noise is employed to extract the functional information for state estimation purposes. The communication between a sensor node and its neighboring ones is implemented over a Rayleigh fading channel. For each binary sensor, a distributed filter is constructed by virtue of the available information from itself and its neighboring nodes, and the overall filtering error dynamics is guaranteed to be exponentially ultimately bounded in the mean square sense. By resorting to a local performance analysis method, sufficient criteria are established for ensuring the existence of the desired distributed filter in terms of a set of recursive linear matrix inequalities. The desired filter parameters are recursively calculated on every node by solving an optimization problem at each time instant with the aim of improving the estimation accuracy. Finally, some comparative results are presented to demonstrate the applicability and effectiveness of the developed distributed filtering scheme.

Translated text

Key words

Fading channels,Linear matrix inequalities,Performance analysis,Estimation,Petroleum,Information processing,Distribution functions,Binary measurement,distributed filtering,local performance analysis,Rayleigh fading channel,sensor network

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper