Sparsity Constrained Deconvolution Approaches for Acoustic Source Mapping

Yardibi, Tarik; Li, Jian; Stoica, Petre; Cattafesta, Louis N.

doi:10.2514/6.2008-2956

Cited by 40 publications

(59 citation statements)

References 30 publications

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“…For broadband noise, of which the acoustic energy is more equally distributed over the mode orders, this may not be the most appropriate approach. This paper proposes a strategy based on deconvolution, using lessons learned from methods that were developed over the past decade for microphone array measurements [13][14][15][16][17][18][19] , and exploiting the fact that the DFT method for azimuthal mode detection is exactly the same as Conventional Beamforming (CB) with microphone arrays 20 . Deconvolution methods start with CB and aim at retrieving source amplitudes using the known CB response of individual sources.…”

Section: Nomenclaturementioning

confidence: 99%

Deconvolution of Azimuthal Mode Detection Measurements

Sijtsma¹,

Brouwer

2017

23rd AIAA/CEAS Aeroacoustics Conference

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Unequally spaced transducer rings make it possible to extend the range of detectable azimuthal modes. The other side of the coin is that the response of the mode detection algorithm to a single mode is distributed over all detectable modes, similarly to the Point Spread Function of Conventional Beamforming with microphone arrays. With multiple modes the response patterns interfere, leading to a relatively high "noise floor" of spurious modes in the detected mode spectrum, in other words, to a low dynamic range. In this paper a deconvolution strategy is proposed for increasing this dynamic range. It starts with separating the measured sound into shaft tones and broadband noise. For broadband noise modes, a standard Non-Negative Least Squares solver appeared to be a perfect deconvolution tool. For shaft tones a Matching Pursuit approach is proposed, taking advantage of the sparsity of dominant modes. The deconvolution methods were applied to mode detection measurements in an AneCom fan rig. An increase in dynamic range of typically 10 to 15 dB was found.

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Section: Nomenclaturementioning

confidence: 99%

Deconvolution of Azimuthal Mode Detection Measurements

Sijtsma¹,

Brouwer

2017

23rd AIAA/CEAS Aeroacoustics Conference

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“…The location of each senor is accurately known, but each location has been randomly selected, for example due to logistics, topography, or currents. Examples of such networks include seismics, [1][2][3] ocean acoustics, 5,6 air acoustics, 7,8 and speech communication applications. 9 The sensors used here are assumed to be fixed, but for frequency domain beamforming it is possible to adjust the complex-valued weighting of each sensor to give well-defined beampatterns.…”

Section: Introductionmentioning

confidence: 99%

Improving beampatterns of two-dimensional random arrays using convex optimization

Gerstoft¹,

Hodgkiss²

2011

The Journal of the Acoustical Society of America

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Sensors are becoming ubiquitous and can be combined in arrays for source localization purposes. If classical conventional beamforming is used, then random arrays have poor beampatterns. By pre-computing sensor weights, these beampatterns can be improved significantly. The problem is formulated in the frequency domain as a desired look direction, a frequency-independent transition region, and the power minimized in a rejection-region. Using this formulation, the frequency-dependent sensor weights can be obtained using convex optimization. Since the weights are data independent they can be pre-computed, the beamforming has similar computational complexity as conventional beamforming. The approach is demonstrated for real 2D arrays.

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“…Simple integration techniques can be used to provide approximate field values, but in general the inverse array problem of deconvolution must be solved to extract quantitative information from array data. Many frequency-domain deconvolution algorithms exist for incoherent source fields, for example DAMAS, 1 DAMAS2, 2 SC-DAMAS, 3 and CLEAN-SC. 4 However, when a source field contains regions of coherence, there are fewer algorithm selections.…”

Section: Introductionmentioning

confidence: 99%

Wavespace-Based Coherent Deconvolution

Bahr

Cattafesta

2012

18th AIAA/CEAS Aeroacoustics Conference (33rd AIAA Aeroacoustics Conference)

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Array deconvolution is commonly used in aeroacoustic analysis to remove the influence of a microphone array's point spread function from a conventional beamforming map. Unfortunately, the majority of deconvolution algorithms assume that the acoustic sources in a measurement are incoherent, which can be problematic for some aeroacoustic phenomena with coherent, spatially-distributed characteristics. While several algorithms have been proposed to handle coherent sources, some are computationally intractable for many problems while others require restrictive assumptions about the source field. Newer generalized inverse techniques hold promise, but are still under investigation for general use. An alternate coherent deconvolution method is proposed based on a wavespace transformation of the array data. Wavespace analysis offers advantages over curved-wave array processing, such as providing an explicit shift-invariance in the convolution of the array sampling function with the acoustic wave field. However, usage of the wavespace transformation assumes the acoustic wave field is accurately approximated as a superposition of plane wave fields, regardless of true wavefront curvature. The wavespace technique leverages Fourier transforms to quickly evaluate a shift-invariant convolution. The method is derived for and applied to ideal incoherent and coherent plane wave fields to demonstrate its ability to determine magnitude and relative phase of multiple coherent sources. Multiscale processing is explored as a means of accelerating solution convergence. A case with a spherical wave front is evaluated. Finally, a trailing edge noise experiment case is considered. Results show the method successfully deconvolves incoherent, partially-coherent, and coherent plane wave fields to a degree necessary for quantitative evaluation. Curved wave front cases warrant further investigation. A potential extension to nearfield beamforming is proposed.

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Sparsity Constrained Deconvolution Approaches for Acoustic Source Mapping

Cited by 40 publications

References 30 publications

Deconvolution of Azimuthal Mode Detection Measurements

Deconvolution of Azimuthal Mode Detection Measurements

Improving beampatterns of two-dimensional random arrays using convex optimization

Wavespace-Based Coherent Deconvolution

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