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Electronics 2017, 6(4), 76; https://doi.org/10.3390/electronics6040076

Quiet Zone Enhancement for a Target Location Using an Improved Virtual Sensing Algorithm

Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education, Cheonan, Chungnam 330-708, Korea
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Received: 22 August 2017 / Revised: 15 September 2017 / Accepted: 28 September 2017 / Published: 3 October 2017
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Abstract

Generating a quiet zone at a target location is the ultimate goal of active noise control (ANC). Generally, the best noise cancellation is achieved at the locations of error sensors. However, the distribution of physical error sensors at a given location is not always convenient or feasible. To overcome this challenge, a number of virtual sensing algorithms for ANC have been researched. Using the physical error signals and knowledge of the system, the adaptive least mean square (LMS) virtual sensing algorithm estimates the error signal at a location that is remote from the physical error sensor, referred to as the virtual location. It achieves excellent performance under the assumption that the unknown primary path is fixed, but its performance decreases significantly if the path is constantly changing. This paper presents a real-time ANC system with an improved virtual sensing algorithm to solve this problem. The proposed system was tested to enhance the quiet zone in a constantly changing environment; an enhanced quiet zone was created, and the noise cancellation, especially at the target location, obviously improved. View Full-Text
Keywords: active noise control (ANC); virtual sensing; quiet zone active noise control (ANC); virtual sensing; quiet zone
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mu, X.; Rheem, J. Quiet Zone Enhancement for a Target Location Using an Improved Virtual Sensing Algorithm. Electronics 2017, 6, 76.

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