Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism

This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-...

ver descrição completa

Detalhes bibliográficos
Autores: Hu, Jun, Hu, Zhibin, Caballero-Águila, Raquel, Chen, Cai, Fan, Shuting, Yi, Xiaojian
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2023
País:España
Recursos:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4593
Acesso em linha:https://doi.org/10.1016/j.ins.2023.118950
https://hdl.handle.net/10953/4593
Access Level:acceso abierto
Palavra-chave:Nonlinear time-varying multi-sensor networked systems
Multiple missing measurements
Dynamic event-triggered communication
Distributed resilient fusion filtering
Inverse covariance intersection
id ES_d59dea679edbe7f0fa6ddce0b06d61b9
oai_identifier_str oai:ruja.ujaen.es:10953/4593
network_acronym_str ES
network_name_str España
repository_id_str
spelling Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanismHu, JunHu, ZhibinCaballero-Águila, RaquelChen, CaiFan, ShutingYi, XiaojianNonlinear time-varying multi-sensor networked systemsMultiple missing measurementsDynamic event-triggered communicationDistributed resilient fusion filteringInverse covariance intersectionThis paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.Elsevier202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.ins.2023.118950https://hdl.handle.net/10953/4593reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésInformation SciencesAn error occurred on the license name.An error occurred getting the license - uri.info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/45932026-06-24T12:41:07Z
dc.title.none.fl_str_mv Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
title Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
spellingShingle Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
Hu, Jun
Nonlinear time-varying multi-sensor networked systems
Multiple missing measurements
Dynamic event-triggered communication
Distributed resilient fusion filtering
Inverse covariance intersection
title_short Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
title_full Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
title_fullStr Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
title_full_unstemmed Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
title_sort Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism
dc.creator.none.fl_str_mv Hu, Jun
Hu, Zhibin
Caballero-Águila, Raquel
Chen, Cai
Fan, Shuting
Yi, Xiaojian
author Hu, Jun
author_facet Hu, Jun
Hu, Zhibin
Caballero-Águila, Raquel
Chen, Cai
Fan, Shuting
Yi, Xiaojian
author_role author
author2 Hu, Zhibin
Caballero-Águila, Raquel
Chen, Cai
Fan, Shuting
Yi, Xiaojian
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Nonlinear time-varying multi-sensor networked systems
Multiple missing measurements
Dynamic event-triggered communication
Distributed resilient fusion filtering
Inverse covariance intersection
topic Nonlinear time-varying multi-sensor networked systems
Multiple missing measurements
Dynamic event-triggered communication
Distributed resilient fusion filtering
Inverse covariance intersection
description This paper investigates the distributed resilient fusion filtering (DRFF) issue under inverse covariance intersection (ICI) fusion criterion and dynamic event-triggered mechanisms (DETMs), where the physical plant is described by stochastic nonlinear multi-sensor networked systems (MSNSs) with time-varying system parameters and multiple missing measurements (MMMs). The measurements from various sensor nodes to the fusion center may undergo the missing data, where this phenomenon is depicted by means of random variables governed by certain statistical principles. In addition, the DETM is adopted to regulate the communication process from each sensor node to fusion center, which can alleviate the network transmission situations with communication overload and energy consumption limitation. The purpose of the addressed issue is to construct a set of local resilient filters (LRFs) for stochastic nonlinear MSNSs with MMMs via the DETM, which can guarantee that the minimized upper bounds are derived and the desirable filter gain with easy-to-implementation form is given. Subsequently, via the obtained LRFs, a unified framework of the DRFF approach is formulated through using the ICI fusion criterion. In addition, the monotonicity analysis of the obtained upper bound in regard to the triggered parameter is examined by providing rigorous theoretical proof. Finally, the simulations with comparison experiment are provided to illustrate the validity of presented DRFF technique.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.ins.2023.118950
https://hdl.handle.net/10953/4593
url https://doi.org/10.1016/j.ins.2023.118950
https://hdl.handle.net/10953/4593
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information Sciences
dc.rights.none.fl_str_mv An error occurred on the license name.
An error occurred getting the license - uri.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv An error occurred on the license name.
An error occurred getting the license - uri.
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420740926767104
score 15,812429

Registos relacionados