M
Mats Viberg
Researcher at Chalmers University of Technology
Publications - 232
Citations - 12570
Mats Viberg is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Sensor array & Estimation theory. The author has an hindex of 41, co-authored 231 publications receiving 11749 citations. Previous affiliations of Mats Viberg include Linköping University & Blekinge Institute of Technology.
Papers
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Proceedings ArticleDOI
Simultaneous information and power transfer under a non-linear RF energy harvesting model
TL;DR: This work proposes a practical quadratic model for the power conversion efficiency in EH circuitry and uses it to investigate the problem of resource allocation for a multiuser Orthogonal Frequency-Division Multiple Access (OFDMA) system.
Proceedings ArticleDOI
Robust signal parameter estimation in the presence of array perturbations
TL;DR: Signal parameter estimators which are less sensitive to perturbations in the array manifold are presented and a compact expression for the MAP Cramer-Rao bound (CRB) on the signal and array parameter estimates is derived.
Proceedings ArticleDOI
Adaptive neural nets filter using a recursive Levenberg-Marquardt search direction
TL;DR: A recursive Levenberg-Marquardt (LM) search direction is proposed as the training algorithm for non-linear adaptive filters, which use multi-layer feed forward neural nets as the filter structures.
Proceedings ArticleDOI
Analysis of subspace fitting based methods for sensor array processing
Bjorn Ottersten,Mats Viberg +1 more
TL;DR: The problem of estimating signal parameters from sensor array measurements is addressed and a general multidimensional signal subspace method, called the weighted subspace fitting (WSF), is proposed, resulting in a method that always outperforms ML.
Proceedings ArticleDOI
A statistical perspective on state-space modeling using subspace methods
TL;DR: The authors investigate aspects of subspace-based state-space identification techniques from a statistical perspective and find that the subspace technique may be a strong candidate for determining initial values for the optimization in the efficient PE method.