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- W2135552943 abstract "We consider a fusion sensor-network architecture where sensor-processor elements (nodes) observe a spatially correlated random field within a region of interest and transmit quantized observations to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. We assume that the observations follow a linear-regression model with known field correlations and propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the field and its parameters. We consider local quantile and Lloyd-Max quantizers at the nodes; both quantization schemes are based on approximate predictive measurement distributions, constructed using the feedback information from the fusion center. We also apply our estimation approach to the no-feedback scenario and present numerical examples demonstrating the performance of the proposed methods." @default.
- W2135552943 created "2016-06-24" @default.
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- W2135552943 date "2008-10-01" @default.
- W2135552943 modified "2023-09-23" @default.
- W2135552943 title "Estimating a random field in sensor networks using quantized spatially correlated data" @default.
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- W2135552943 doi "https://doi.org/10.1109/acssc.2008.5074769" @default.
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