Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6959975 | Signal Processing | 2015 | 6 Pages |
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 explicitly 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Alexander Bertrand, Marc Moonen,