TITLE: Distributed adaptive generalized eigenvector estimation of a sensor signal covariance matrix pair in a fully-connected sensor network
AUTHORS: Alexander Bertrand and Marc Moonen
ABSTRACT: The generalized eigenvalue decomposition (GEVD) of a pair of matrices generalizes the concept of the eigenvalue decomposition (EVD) of a single matrix. It is a widely-used tool in signal processing applications, in particular in a context of spatial filtering and subspace estimation. In this paper, we describe a distributed adaptive algorithm to estimate generalized eigenvectors (GEVCs) of a pair of sensor signal covariance matrices in a fully-connected wireless sensor network. The algorithm computes these GEVCs in an iterative fashion without explicitely constructing the full network-wide covariance matrices. Instead, the nodes only exchange compressed sensor signal observations, providing a significant reduction in per-node communication and computational cost compared to the scenario where all the raw sensor signal observations are collected and processed in a fusion center.
STATUS: Signal Processing, vol. 106, pp. 209-214, Jan. 2015.(PDF, BibTeX entry)