• 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