Distributed adaptive estimation of correlated node-specific signals in a fully connected sensor network

We introduce a distributed adaptive estimation algorithm operating in an ideal fully connected sensor network. The algorithm estimates node-specific signals at each node based on reduced-dimensionality sensor measurements of other nodes in the network. If the node-specific signals to be estimated are linearly dependent on a common latent process with a low dimension compared to the dimension of the sensor measurements, the algorithm can significantly reduce the required communication bandwidth and still provide the optimal linear estimator at each node as if all sensor measurements were available in every node. Because of its adaptive nature and fast convergence properties, the algorithm is suited for real-time applications in dynamic environments, such as speech enhancement in acoustic sensor networks.