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
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;
Conference_Titel :
Information Theory and Applications Workshop (ITA), 2014
Conference_Location :
San Diego, CA
DOI :
10.1109/ITA.2014.6804232