Greedy Sparse Array Design for Optimal Localization under Spatially Prioritized Source Distribution

Gershon, Yotam; Buchris, Yaakov; Cohen, Israel

doi:10.1109/icassp40776.2020.9053917

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…In contrast, we consider a scenario where, due to the limited extent of the region of interest, only a subset of the full width of the aperture is of relevance. Similar investigations were carried out in [9] for acoustic source localization using microphones, prioritizing certain regions in the image. In the same spirit, we derive a simpler heuristic.…”

Section: Introductionmentioning

confidence: 80%

Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection

et al. 2021

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In ultrasonic non-destructive testing, array and matrix transducers are being employed for applications that require in-field steerability or which benefit from a higher number of insonification angles. Having many transmit channels, on the other hand, increases the measurement time and renders the use of array transducers unfeasible for many applications. In the literature, methods for reducing the number of required channels compared to the full matrix capture scheme have been proposed. Conventionally, these are based on choosing the aperture that is as wide as possible. In this publication, we investigate a scenario from the field of pipe inspection, where cracks have to be detected in specific areas near the weld. Consequently, the width of the aperture has to be chosen according to the region of interest at hand. On the basis of ray-tracing simulations which incorporate a model of the transducer directivity and beam spread at the interface, we derive application specific measures of the energy distribution over the array configuration for given regions of interest. These are used to determine feasible subsampling schemes. For the given scenario, the validity/quality of the derived subsampling schemes are compared on the basis of reconstructions using the conventional total focusing method as well as sparsity driven-reconstructions using the Fast Iterative Shrinkage-Thresholding Algorithm. The results can be used to effectively improve the measurement time for the given application without notable loss in defect detectability.

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Section: Introductionmentioning

confidence: 80%

Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection

et al. 2021

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“…where Re{•} is the real part of a complex number. The expression shown in (17) is known as the Slepian-Bangs formulation of the FIM [21] and is applicable due to the model statistics. Alternatively, a slight variation of (17) is given by…”

Section: Cramér-rao Boundmentioning

confidence: 99%

“…The effort in sparse array design and optimal sensor position is largely dedicated to choosing a performance metric that promotes the desired imaging behavior. These metrics include, but are not limited to, beam patterns [ 14 , 15 , 17 ], number of virtual sensors [ 18 , 19 ], mutual coherence [ 8 ], information-theoretic quantities [ 16 ], and data-driven quantities [ 20 ]. Similarly to [ 22 ], the approach in this paper focuses on minimizing the variance of the estimate of the location of point-like scatterers.…”

Section: Subsampled Datamentioning

confidence: 99%

“…The solution set can be, for example, restricted to include solutions where the active Tx element in a measurement cycle cannot be used for Rx, avoiding self-interference artifacts. Beyond this, the complexity may still be too large, making greedy algorithms a reasonable (although suboptimal) solution approach [ 17 , 20 ].…”

Section: Sparse Array Designmentioning

confidence: 99%

“…The optimal position of a predefined number of sensors can be sought after in a similar fashion. To this end, in the field of acoustics recent works have focused on the greedy optimization of cost functions such as the mutual information between sensor locations that have already been assigned and those that are still open [ 16 ], or the average size of the main lobe in a Region of Interest (ROI) over which the probability of finding a source is non-uniform [ 17 ]. When paired with convolutional beamforming, the elements in a pair of co-prime arrays may also be placed such that the resulting co-array has a larger aperture and effectively contains all of its elements [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

Perez

Kirchhof

Krieg

et al. 2020

Sensors

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Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

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On the Performance Limits of Array-Based Acoustic Source Localization

Xiao,

Zhou,

et al. 2023

IEEE Sensors J.

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Greedy Sparse Array Design for Optimal Localization under Spatially Prioritized Source Distribution

Cited by 4 publications

References 11 publications

Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection

Locally Optimal Subsampling Strategies for Full Matrix Capture Measurements in Pipe Inspection

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

On the Performance Limits of Array-Based Acoustic Source Localization

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