Information fusion strategies and performance bounds in packet-drop networks

DOI10.1016/j.automatica.2011.02.002zbMath1219.93121OpenAlexW1984277564MaRDI QIDQ2276098

Luca Schenato, Alessandro Chiuso

Publication date: 1 August 2011

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2011.02.002

zbMATH Keywords

boundssensor fusionpacket lossdistributed estimationmultiple sensorsminimum square estimatorssmart sensors

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Stochastic stability in control theory (93E15)

Related Items (8)

One-channel networked data fusion with communication constraint ⋮ Distributed Kalman filtering fusion with packet loss or intermittent communications from local estimators to fusion center ⋮ Model based peer-to-peer estimator over wireless sensor networks with lossy channels ⋮ Modeling and estimation for networked systems with multiple random transmission delays and packet losses ⋮ Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises ⋮ Multi-UAVs cooperative localization algorithms with communication constraints ⋮ Information fusion strategies and performance bounds in packet-drop networks ⋮ Decentralised - filtering of networked control systems: a jump system approach

Cites Work

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• A scattering framework for decentralized estimation problems
• Information fusion strategies and performance bounds in packet-drop networks
• Combining and updating of local estimates and regional maps along sets of one-dimensional tracks
• Optimal Output Feedback Control Using Two Remote Sensors Over Erasure Channels
• The problem of state estimation via asynchronous communication channels with irregular transmission times
• Kalman Filtering With Intermittent Observations
• Optimal update with out-of-sequence measurements

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