A Kalman filter approach to virtual sensing for active noise control

Petersen, Dick; Fraanje, Rufus; Cazzolato, B.; Zander, Anthony C.; Hansen, Colin H.

doi:10.1016/j.ymssp.2007.06.007

Cited by 85 publications

(66 citation statements)

References 10 publications

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“…3,4 In recent years, different approaches have been suggested to realize real-time active noise control (see, e.g., Refs. [5][6][7][8][9][10]. Most of them exploit the least mean square (LMS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Potential-based methodology for active sound control in three dimensional settings

Lim

Utyuzhnikov

Lam

et al. 2014

The Journal of the Acoustical Society of America

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This paper extends a potential-based approach to active noise shielding with preservation of wanted sound in three-dimensional settings. The approach, which was described in a previous publication [Lim et al., J. Acoust. Soc. Am. 129(2), 717-725 (2011)], provides several significant advantages over conventional noise control methods. Most significantly, the methodology does not require any information including the characterization of sources, impedance boundary conditions and surrounding medium, and that the methodology automatically differentiates between the wanted and unwanted sound components. The previous publication proved the concept in one-dimensional conditions. In this paper, the approach for more realistic conditions is studied by numerical simulation and experimental validation in three-dimensional cases. The results provide a guideline to the implementation of the active shielding method with practical three-dimensional conditions. Through numerical simulation it is demonstrated that while leaving the wanted sound unchanged, the developed approach offers selective volumetric noise cancellation within a targeted domain. In addition, the method is implemented in a three-dimensional experiment with a white noise source in a semi-anechoic chamber. The experimental study identifies practical difficulties and limitations in the use of the approach for real applications.

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Section: Introductionmentioning

confidence: 99%

“…5,10 There have also been a few attempts to apply the virtual sensing and surface integral control to tackle this problem. 8,9 All of them are based on trying to predict the wanted sound component and, therefore, are quite limited because the wanted ingredient cannot completely be separated from the total acoustic field.…”

Section: Introductionmentioning

confidence: 99%

Potential-based methodology for active sound control in three dimensional settings

Lim

Utyuzhnikov

Lam

et al. 2014

The Journal of the Acoustical Society of America

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“…However, there has been demand to control the noise at a remote location where it is not feasible to place a microphone, referred to as the virtual location. This has been achieved by modifying the FXLMS algorithm in a number of ways [2][3][4][5][6][7][8]. The most popular virtual ANC algorithms are the virtual microphone arrangement [3], the remote microphone technique [4], the forward-difference prediction technique [5], the adaptive LMS virtual microphone technique [6], the Kalman filtering virtual sensing technique [7] and the stochastically optimal tonal diffuse field (SOTDF) virtual sensing method [8].…”

Section: Introductionmentioning

confidence: 99%

“…This has been achieved by modifying the FXLMS algorithm in a number of ways [2][3][4][5][6][7][8]. The most popular virtual ANC algorithms are the virtual microphone arrangement [3], the remote microphone technique [4], the forward-difference prediction technique [5], the adaptive LMS virtual microphone technique [6], the Kalman filtering virtual sensing technique [7] and the stochastically optimal tonal diffuse field (SOTDF) virtual sensing method [8]. The remote microphone technique [4], seen to be the most effective virtual sensing algorithm, uses the offline identification of two secondary paths: one between the control signal and the physical microphone position and the other between the control signal and the virtual microphone position.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Das

Moreau

Cazzolato

2013

Mechanical Systems and Signal Processing

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The computational complexity of the virtual FXLMS algorithm is higher than that of the conventional FXLMS algorithm. The additional complexity comes from computation of three secondary path transfer functions (as opposed to one) and a transfer function between the physical and the virtual microphones. The order of these transfer functions may be very high in practical situations where the acoustic damping is low. The high computational complexity of the virtual FXLMS algorithm imposes issues like high power consumption, making it difficult to implement the algorithm in battery operated ANC devices such as active headsets. In addition, the operating sampling frequency of the algorithm is limited and this in turn restricts its operation to relatively low frequency applications. In this paper, a new virtual FXLMS algorithm is derived by implementing all of the secondary path transfer functions in the frequency domain. The algorithm is simulated using measured transfer functions in a duct with low acoustic damping. Implementation schemes are proposed for the new frequency-domain virtual FXLMS algorithm, citing its advantages for use as an efficient real-time active noise control algorithm.Index Terms: Active noise control, adaptive filter, frequency-domain adaptive filter, computational complexity, virtual ANC. 2

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“…This method was later extended by using the adaptive leastmean-square (LMS) virtual technique (Cazzolato, 2002) in which an adaptive LMS algorithm was used to obtain the optimal microphone weights for the extrapolation. Based on the optimal state estimation, Petersen et al (2008) utilized a Kalman filter to design a virtual sensor for active noise control system. There was also virtual sensing work conducted for a diffused sound field system (Moreau et al, 2009) or for moving virtual sensing (Petersen et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Virtual sensors for active noise control in acoustic–structural coupled enclosures using structural sensing: Robust virtual sensor design

Halim

2011

The Journal of the Acoustical Society of America

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The work was aimed to develop a robust virtual sensing design methodology for sensing and active control applications of vibro-acoustic systems. The proposed virtual sensor was designed to estimate a broadband acoustic interior sound pressure using structural sensors, with robustness against certain dynamic uncertainties occurring in an acoustic-structural coupled enclosure. A convex combination of Kalman sub-filters was used during the design, accommodating different sets of perturbed dynamic model of the vibro-acoustic enclosure. A minimax optimization problem was set up to determine an optimal convex combination of Kalman sub-filters, ensuring an optimal worst-case virtual sensing performance. The virtual sensing and active noise control performance was numerically investigated on a rectangular panel-cavity system. It was demonstrated that the proposed virtual sensor could accurately estimate the interior sound pressure, particularly the one dominated by cavitycontrolled modes, by using a structural sensor. With such a virtual sensing technique, effective active noise control performance was also obtained even for the worst-case dynamics.

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A Kalman filter approach to virtual sensing for active noise control

Cited by 85 publications

References 10 publications

Potential-based methodology for active sound control in three dimensional settings

Potential-based methodology for active sound control in three dimensional settings

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Virtual sensors for active noise control in acoustic–structural coupled enclosures using structural sensing: Robust virtual sensor design

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