DocumentCode
3155166
Title
Power iteration-based distributed total least squares estimation in ad hoc sensor networks
Author
Bertrand, Alexander ; Moonen, Marc
Author_Institution
Future Health Dept., Univ. of Leuven, Leuven, Belgium
fYear
2012
fDate
25-30 March 2012
Firstpage
2669
Lastpage
2672
Abstract
In this paper, we revisit the distributed total least squares (D-TLS) algorithm, which operates in an ad hoc sensor network where each node has access to a subset of the equations of an overdetermined set of linear equations. The D-TLS algorithm computes the total least squares (TLS) solution of the full set of equations in a fully distributed fashion (without fusion center). We modify the D-TLS algorithm to eliminate the large computational complexity due to an eigenvalue decomposition (EVD) at every node and in each iteration. In the modified algorithm, a single power iteration (PI) is performed instead of a full EVD computation, which significantly reduces the computational complexity. Since the nodes then do not exchange their true eigenvectors, the theoretical convergence results of the original D-TLS algorithm do not hold anymore. Nevertheless, we find that this PI-based D-TLS algorithm still converges to the network-wide TLS solution, under certain assumptions, which are often satisfied in practice. We provide simulation results to demonstrate the convergence of the algorithm, even when some of these assumptions are not satisfied.
Keywords
ad hoc networks; computational complexity; convergence of numerical methods; eigenvalues and eigenfunctions; estimation theory; iterative methods; least squares approximations; wireless sensor networks; EVD computation; PI-based D-TLS algorithm; ad hoc sensor networks; computational complexity; eigenvalue decomposition; eigenvectors; fully distributed set of equations; linear equations; network-wide TLS solution; power iteration-based distributed total least squares estimation; Ad hoc networks; Convergence; Eigenvalues and eigenfunctions; Equations; Least squares approximation; Signal processing algorithms; Vectors; Distributed estimation; total least squares; wireless sensor networks (WSNs);
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location
Kyoto
ISSN
1520-6149
Print_ISBN
978-1-4673-0045-2
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2012.6288466
Filename
6288466
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