Optimal distributed estimation in wireless sensor networks with spatially correlated noise sources

A sensor network's motes observe the environment, make estimates based on observations with spatially correlated noise sources, and then send/relay these estimates to a Cluster-Head (CH). A novel scheme based on dithered quantization and channel compensation is used to ensure that each mote's local estimate received by the CH is unbiased. Based on an upper bound of the noise covariance matrix, the CH fuses these unbiased local estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We evaluate the mean square error(MSE) of the final estimate by both analysis and simulation.

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