Distributed robust Kalman filtering with unknown and noisy parameters in sensor networks

DOI10.1155/2018/7954263zbMath1417.93308OpenAlexW2902773765WikidataQ128892908 ScholiaQ128892908MaRDI QIDQ1727260

Ya Zhang, Cheng-Lin Liu, Dong-Hua Chen, Yang-Yang Chen

Publication date: 20 February 2019

Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/7954263

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Filtering in stochastic control theory (93E11)

Cites Work

• Stability of consensus extended Kalman filter for distributed state estimation
• Mean square state estimation for sensor networks
• Distributed consensus-based estimation considering network induced delays and dropouts
• Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability
• Decentralized observers with consensus filters for distributed discrete-time linear systems
• Stochastic sensor activation for distributed state estimation over a sensor network
• Distributed robust filtering with \(H_\infty\) consensus of estimates
• Robust \(H_{\infty}\) filtering with error variance constraints for discrete time-varying systems with uncertainty
• Distributed Kalman consensus filter with intermittent observations
• Distributed robust estimation over randomly switching networks using \(H_{\infty }\) consensus
• Consensus based overlapping decentralized estimation with missing observations and communication faults
• Consensus-based linear distributed filtering
• A Global Detectability Condition for Consensus Tracking of Linear Multi-Agent Systems with Stochastic Disturbances
• Consensus-Based Linear and Nonlinear Filtering
• Local Condition Based Consensus Filtering With Stochastic Nonlinearities and Multiple Missing Measurements
• Diffusion Strategies for Distributed Kalman Filtering and Smoothing
• Robust Finite-Horizon Kalman Filtering for Uncertain Discrete-Time Systems

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