Robust fusion Kalman estimators for networked mixed uncertain systems with random one-step measurement delays, missing measurements, multiplicative noises and uncertain noise variances

DOI10.1016/j.ins.2020.04.044zbMath1461.93508OpenAlexW3025807294MaRDI QIDQ2023189

Chenjian Ran, Deng, Zili

Publication date: 3 May 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.04.044

zbMATH Keywords

augmented state approachextended Lyapunov equation approachintegrated covariance intersection fusion approachminimax robust fusion Kalman estimatornetworked mixed uncertain system

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Sensitivity (robustness) (93B35) Control/observation systems with incomplete information (93C41) Networked control (93B70)

Related Items (9)

Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol ⋮ Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates ⋮ Robust fusion filter for multisensor descriptor system with uncertain‐variance noises and packet dropout ⋮ Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays ⋮ Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties ⋮ Fusion filtering for rectangular descriptor systems with stochastic bias and random observation delays under weighted try-once-discard protocol ⋮ Distributed fusion robust filter for multisensor networked singular control system with uncertain variances and missing measurement ⋮ Robust sequential fusion Kalman estimators with asymptotic equivalence and stability for networked uncertain sensor systems ⋮ Distributed resilient fusion filtering for nonlinear systems with multiple missing measurements via dynamic event-triggered mechanism

Cites Work

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