Distributed GEVD-based signal subspace estimation in a fully-connected wireless sensor network. Hassani, A., Bertrand, A., & Moonen, M. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 1292-1296, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

In this paper, we present a distributed algorithm for network-wide signal subspace estimation in a fully-connected wireless sensor network with multi-sensor nodes. We consider scenarios where the noise field is spatially correlated between the nodes. Therefore, rather than an eigenvalue decomposition (EVD-) based approach, we apply a generalized EVD (GEVD-) based approach which allows to directly incorporate the (estimated) noise covariance. Furthermore, the GEVD is also immune to unknown per-channel scalings. We first use a distributed algorithm to estimate the principal generalized eigenvectors (GEVCs) of a pair of network-wide sensor signal covariance matrices, without explicitly constructing these matrices, as this would inherently require data centralization. We then apply a transformation at each node to extract the actual signal subspace estimate from the principal GEVCs. The resulting distributed algorithm can reduce the per-node communication and computational cost. We demonstrate the effectiveness of the algorithm by means of numerical simulations.

@InProceedings{6952458, author = {A. Hassani and A. Bertrand and M. Moonen}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {Distributed GEVD-based signal subspace estimation in a fully-connected wireless sensor network}, year = {2014}, pages = {1292-1296}, abstract = {In this paper, we present a distributed algorithm for network-wide signal subspace estimation in a fully-connected wireless sensor network with multi-sensor nodes. We consider scenarios where the noise field is spatially correlated between the nodes. Therefore, rather than an eigenvalue decomposition (EVD-) based approach, we apply a generalized EVD (GEVD-) based approach which allows to directly incorporate the (estimated) noise covariance. Furthermore, the GEVD is also immune to unknown per-channel scalings. We first use a distributed algorithm to estimate the principal generalized eigenvectors (GEVCs) of a pair of network-wide sensor signal covariance matrices, without explicitly constructing these matrices, as this would inherently require data centralization. We then apply a transformation at each node to extract the actual signal subspace estimate from the principal GEVCs. The resulting distributed algorithm can reduce the per-node communication and computational cost. We demonstrate the effectiveness of the algorithm by means of numerical simulations.}, keywords = {covariance matrices;distributed algorithms;eigenvalues and eigenfunctions;signal processing;wireless sensor networks;fully-connected wireless sensor network;network-wide signal subspace estimation;generalized eigenvalue decomposition;noise covariance;principal generalized eigenvectors;GEVC;sensor signal covariance matrices;data centralization;GEVD estimation;distributed algorithm;numerical simulations;Estimation;Covariance matrices;Noise;Wireless sensor networks;Distributed algorithms;Eigenvalues and eigenfunctions;Wireless sensor network (WSN);distributed estimation;signal subspace estimation;generalized eigenvalue decomposition (GEVD)}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569921765.pdf}, }

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