Many array-processing algorithms or applications require the estimation of a target signal subspace, e.g., for source local- ization or for signal enhancement. In wireless sensor net- works, the straightforward estimation of a network-wide sig- nal subspace would require a centralization of all the sensor signals to compute network-wide covariance matrices. In this paper, we present a distributed algorithm for network-wide signal subspace estimation in which such data centralization is avoided. The algorithm relies on a generalized eigenvalue decomposition (GEVD), which allows to estimate a target sig- nal subspace in spatially correlated noise. We show that the network-wide signal subspace can be found from the inver- sion of the matrices containing the generalized eigenvectors of a pair of reduced-dimension sensor signal covariance ma- trices at each node. The resulting distributed algorithm re- duces the per-node communication and computational cost, while converging to the centralized solution. Numerical sim- ulations reveal a faster convergence speed compared to a pre- viously proposed algorithm.
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