Experimental example of the pseudo-forces method used in characterisation of a structure-borne sound source

Janssens, M.H.A.; Verheij, JW Jan; Loyau, Thierry

doi:10.1016/s0003-682x(01)00023-8

Cited by 31 publications

(14 citation statements)

References 2 publications

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“…This parameter selection will also typically depend on the plant matrix, the singular values and the measurement noise. Finding the best truncation order K is not easy and specific methods are designed for that specific purpose [7,9]. Typical truncated singular value decomposition (TSVD) filter factor shapes are shown in Fig.…”

Section: Truncated Singular Value Decompositionmentioning

confidence: 99%

“…In direct regularization methods such as TSVD and Tikhonov regularization, the selection of the best regularization parameter K or λ is not a trivial task [5,7,9]: it depends on both the regularization strategy and the measured data. Several regularization parameter selection methods in acoustics have been investigated by Nelson and Yoon [4].…”

Section: Direct Definition Of the Filter Factors Using Signal-to-noismentioning

confidence: 99%

“…Hence, for the proposed method, G, p and σ noise must be known in order to implement this regularization. Note that this thresholding regularization method is different to the ones reported in [7,9] since in these references the authors seek for a rejection threshold that would give the best TSVD order K.…”

Section: Direct Definition Of the Filter Factors Using Signal-to-noismentioning

confidence: 99%

“…Among the most common techniques for which commercial systems currently exists, one finds: beamforming [1,2], nearfield acoustical holography (NAH) [3] and inverse methods [4,5,6,7,8,9]. One advantage of the inverse approach and conformal NAH for arbitrary geometries [3] is the possible use of non-uniform or source-conformal microphone arrays.…”

Section: Introductionmentioning

confidence: 99%

“…The second method presented by Thite and Thomson [9] is based on the idea that if a given singular value of G (plant matrix) contributes to the measured response less than the expected measurement noise, it should be discarded. This is formalized through a criterion that compares the estimated noise level with the actual signal level scaled by the first singular value of the plant matrix [9]. For the aforementioned methods, the singular value rejection operates from a given singular value order i up to the system rank.…”

Section: Introductionmentioning

confidence: 99%

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Acoustical inverse problems regularization: Direct definition of filter factors using Signal-to-Noise Ratio

Gauthier

Gérard

Camier

et al. 2014

Journal of Sound and Vibration

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1377Acoustic imaging aims at localization and characterization of sound sources using microphone arrays. In this paper a new regularization method for acoustic imaging by inverse approach is proposed. The method first relies on the singular value decomposition of the plant matrix and on the projection of the measured data on the corresponding singular vectors. In place of regularization using classical methods such as truncated singular value decomposition and Tikhonov regularization, the proposed method involves the direct definition of the filter factors on the basis of a thresholding operation, defined from the estimated measurement noise. The thresholding operation is achieved using modified filter functions. It has the advantage of simplifying the selection of the best regularization amount. Theoretical results show that this method is promising, in terms of ease of implementation and accuracy of results, in comparison with Tikhonov regularization and truncated singular value decomposition.

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Section: Truncated Singular Value Decompositionmentioning

confidence: 99%