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.