@article {Plewe:2020:0736-2935:820,
title = "Analysis of Model Based Virtual Sensing Techniques for Active Noise Control",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2020",
volume = "261",
number = "6",
publication date ="2020-10-12T00:00:00",
pages = "820-831",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2020/00000261/00000006/art00097",
author = "Plewe, Daniel and Agerkvist, Finn T. and Fernandez Grande, Efren and Brunskog, Jonas",
abstract = "The performance of active noise controllers based on adaptive filters like the Filtered Reference Least Mean Square algorithm (FxLMS) is optimal in small zones around the error sensor locations. These locations provide the maximal possible reduction of noise but are not accessible by
people due to the presence of the sensors. Virtual sensing algorithms can be applied to move the optimal zone of control away from the error sensors. Such methods have been investigated during the last three decades and the most of them rely on initial transfer-function estimation with physical
sensors in the virtual locations. This paper investigates how additional physical knowledge about the inherent physics of an active noise control application can be used to derive models, that can extrapolate an arbitrary number of e.g. virtual error sensor signals. A denser grid of error
sensors leads to a more homogeneous reduction of noise in a target area and can extend the frequency range of controllability to higher frequencies. We will introduce the idea of the model based remote microphone technique (MBRMT), which is the motivation for this study and investigate three
models for sound-field extrapolation that could be integrated into the MBRMT.",
}