Distributed signal subspace estimation based on local generalized eigenvector matrix inversion

Amin Hassani, Alexander Bertrand, Marc Moonen
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
Many array-processing algorithms or applications require the estimation of a target signal subspace, e.g., for source localization or for signal enhancement. In wireless sensor networks, the straightforward estimation of a network-wide signal 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
more » ... n is avoided. The algorithm relies on a generalized eigenvalue decomposition (GEVD), which allows to estimate a target signal subspace in spatially correlated noise. We show that the network-wide signal subspace can be found from the inversion of the matrices containing the generalized eigenvectors of a pair of reduced-dimension sensor signal covariance matrices at each node. The resulting distributed algorithm reduces the per-node communication and computational cost, while converging to the centralized solution. Numerical simulations reveal a faster convergence speed compared to a previously proposed algorithm.
doi:10.1109/eusipco.2015.7362611 dblp:conf/eusipco/HassaniBM15 fatcat:qlw52cnotbebfhw6335cm6do3q