• DocumentCode
    133564
  • Title

    Estimation of sensor input signals that are neither bandlimited nor sparse

  • Author

    Bruderer, Lukas ; Loeliger, Hans-Andrea

  • Author_Institution
    Dept. of Inf. Technol. & Electr. Eng., ETH Zurich, Zürich, Switzerland
  • fYear
    2014
  • fDate
    9-14 Feb. 2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The paper addresses the estimation of the continuous-time input signal to a linear sensor that is given in state-space form. In previous work, Bolliger et al. proposed to model the input signal as (continuous-time) white Gaussian noise and derived a corresponding estimator that is based on Kalman filtering. The present paper elaborates on this new estimator. In particular, it establishes the continuity (or the piecewise continuity) of the estimate, presents a new interpolation formula between samples, complements the Kalman-filter perspective by a Wiener-filter perspective, and demonstrates practicality by numerical experiments.
  • Keywords
    Gaussian noise; Kalman filters; Wiener filters; continuous time filters; electric sensing devices; interpolation; state-space methods; white noise; Kalman filtering; Wiener filter; continuous-time input signal estimation; interpolation; linear sensor; state-space form; white Gaussian noise; Covariance matrices; Data models; Estimation; Gaussian noise; Interpolation; Kalman filters; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Applications Workshop (ITA), 2014
  • Conference_Location
    San Diego, CA
  • Type

    conf

  • DOI
    10.1109/ITA.2014.6804232
  • Filename
    6804232