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• W2113163853 abstract "Decentralized physics-based field estimation in clustered sensor networks requires the exchange of state vectors between neighboring clusters. We reduce the communication overhead between clusters by using a differential encoding of state vectors that exploits the spatio-temporal field dependencies. This encoding involves a Kalman prediction step that builds on the state-space equations governing the field's spatio-temporal evolution. The Kalman step keeps the computational complexity low. Simulation results for an acoustic field demonstrate the approach." @default.
• W2113163853 created "2016-06-24" @default.
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• W2113163853 date "2012-03-01" @default.
• W2113163853 modified "2023-09-23" @default.
• W2113163853 title "Predictive state vector encoding for decentralized field estimation in sensor networks" @default.
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• W2113163853 doi "https://doi.org/10.1109/icassp.2012.6288464" @default.
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