Abstract

Traditional local active noise control systems minimise the measured acoustic pressure to generate a zone of quiet at the physical error sensor location. The resulting zone of quiet is generally limited in size and this requires the physical error sensor be placed at the desired location of attenuation, which is often inconvenient. To overcome this, a number of virtual sensing algorithms have been developed for active noise control. Using the physical error signal, the control signal and knowledge of the system, these virtual sensing algorithms estimate the error signal at a location that is remote from the physical error sensor, referred to as the virtual location. Instead of minimising the physical error signal, the estimated error signal is minimised with the active noise control system to generate a zone of quiet at the virtual location. This paper will review a number of virtual sensing algorithms developed for active noise control. Additionally, the performance of these virtual sensing algorithms in numerical simulations and in experiments is discussed and compared.

Highlights

  • Local active noise control systems aim to create a localised zone of quiet at the physical error sensor by minimising the acoustic pressure at the physical error sensor location with secondary sound sources

  • Radcliffe and Gogate [37] demonstrated that theoretically, a perfect estimate of the tonal disturbance at the virtual location can be achieved with this virtual sensing algorithm provided accurate models of the tonal transfer functions are obtained in the preliminary identification stage

  • Fixed virtual sensing algorithms estimate the error signal at a spatially fixed location that is remote from the physical error sensor

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Summary

A Review of Virtual Sensing Algorithms for Active Noise Control

Danielle Moreau 1,? , Ben Cazzolato 1 , Anthony Zander 1 and Cornelis Petersen 2. Ben Cazzolato 1 , Anthony Zander 1 and Cornelis Petersen 2. Author to whom correspondence should be addressed. Received: 29 September 2008 / Accepted: 29 October 2008 / Published: 3 November 2008

Introduction
Local active
Implement the Kalman filtering virtual sensing method as
Conclusion
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