Synthesis of nonuniformly spaced arrays using a general nonlinear minimax optimisation method

Schjær-Jacobsen, Hans; Madsen, Kaj

doi:10.1109/tap.1976.1141369

Cited by 50 publications

(20 citation statements)

References 8 publications

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“…[18][19][20][21][22][23][24][25][26] Optimal weights to minimize main lobe width or maximum side lobe level for an equally spaced linear array can be analytically calculated using Dolph-Chebyshev array weighting.…”

Section: Optimization Of Weights To Minimize the Maximum Side Lobementioning

confidence: 99%

Adaptive near-field beamforming techniques for sound source imaging

Cho¹,

Roan²

2009

The Journal of the Acoustical Society of America

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Phased array signal processing techniques such as beamforming have a long history in applications such as sonar for detection and localization of far-field sound sources. Two sometimes competing challenges arise in any type of spatial processing; these are to minimize contributions from directions other than the look direction and minimize the width of the main lobe. To tackle this problem a large body of work has been devoted to the development of adaptive procedures that attempt to minimize side lobe contributions to the spatial processor output. In this paper, two adaptive beamforming procedures-minimum variance distorsionless response and weight optimization to minimize maximum side lobes-are modified for use in source visualization applications to estimate beamforming pressure and intensity using near-field pressure measurements. These adaptive techniques are compared to a fixed near-field focusing technique ͑both techniques use near-field beamforming weightings focusing at source locations estimated based on spherical wave array manifold vectors with spatial windows͒. Sound source resolution accuracies of near-field imaging procedures with different weighting strategies are compared using numerical simulations both in anechoic and reverberant environments with random measurement noise. Also, experimental results are given for near-field sound pressure measurements of an enclosed loudspeaker.

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Section: Optimization Of Weights To Minimize the Maximum Side Lobementioning

confidence: 99%

Adaptive near-field beamforming techniques for sound source imaging

Cho¹,

Roan²

2009

The Journal of the Acoustical Society of America

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“…Like all of the previously cited papers, our approach is based on allowing elements only on a fixed underlying grid of positions as opposed to what was done in [23]. The approach taken there is that they leave out the weights and search for the element positions that give minimum peak sidelobe levels.…”

Section: Optimization Of Sparse Arraysmentioning

confidence: 99%

“…23 The optimization region in k-space, containing the visible regioneverything inside the radius |k| = 2π/λ except the mainlobe region which is inside a radius of |k| = 2π/λ sin φ 1 (from [19] …”

Section: • Replacementmentioning

confidence: 99%

Sparse Sampling in Array Processing

Holm

Austeng

Iranpour

et al. 2001

Nonuniform Sampling

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Sparsely sampled irregular arrays and random arrays have been used or proposed in several fields such as radar, sonar, ultrasound imaging, and seismics. We start with an introduction to array processing and then consider the combinatorial problem of finding the best layout of elements in sparse 1-D and 2-D arrays. The optimization criteria are then reviewed: creation of beampatterns with low mainlobe width and low sidelobes, or as uniform as possible coarray. The latter case is shown here to be nearly equivalent to finding a beampattern with minimal peak sidelobes.We have applied several optimization methods to the layout problem, including linear programming, genetic algorithms and simulated annealing. The examples given here are both for 1-D and 2-D arrays. The largest problem considered is the selection of K = 500 elements in an aperture of 50 by 50 elements. Based on these examples we propose that an estimate of the achievable peak level in an algorithmically optimized array is inverse proportional to K and is close to the estimate of the average level in a random array.Active array systems use both a transmitter and receiver aperture and they need not necessarily be the same. This gives additional freedom in design of the thinning patterns, and favorable solutions can be found by using periodic patterns with different periodicity for the two apertures, or a periodic pattern in combination with an algorithmically optimized pattern with the condition that there be no overlap between transmitter and receiver elements. With the methods given here one has the freedom to choose a design method for a sparse array system using either the same elements for the receiver and the transmitter, no overlap between the receiver and transmitter or partial overlap as in periodic arrays.

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“…Critical factors affecting performance include acoustic environment, source spectral content, processing algorithm, and microphone geometry. For a fixed number of microphones it has been demonstrated that the array geometry is the dominant factor for performance [3][4][5]. However, previous studies have largely focused on regular geometries in far-field.…”

Section: Introductionmentioning

confidence: 99%

“…In general, most of these analyses have been done for narrow-band far-field cases where spatial aliasing is directly related to microphone spacing and resolution to aperture. Irregular arrays, which diversify microphone positions, can potentially achieve better performance, as demonstrated in [3,4,7]. Instead of limited optimal range of signal frequency for regular arrays, irregular arrays can result in a more consistent performance over a broader range of frequencies, such as those associated with speech [6].…”

Section: Introductionmentioning

confidence: 99%

Geometry descriptors of irregular microphone arrays related to beamforming performance

Donohue

2012

EURASIP J. Adv. Signal Process.

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Performance analysis for microphone arrays with irregular geometries typically requires direct computation of beamforming gains over the spatial and frequency ranges of interest. However, theses computations can be very consuming and limit synthesis methods for applications that require rapid answers, as in the case of surveillance and mobile platforms. A better understanding of microphone arrangements and their impact on performance can result in more efficient objective functions for optimizing array performance. This article, therefore, analyzes the relationship between irregular microphone geometries and spatial filtering performance with Monte Carlo simulations. Novel geometry descriptors are developed to capture the properties of irregular microphone distributions showing their impact on array performance. Performance metrics are computed from three-dimensional beam patterns through a delay and sum beamformer with a fixed number of microphones for irregular arrays and comparable regular arrays. Statistical analysis and Multi-way Analysis of Variance establish relationships between key performance metrics and proposed geometry descriptors. It is demonstrated that in conjunction with array centroid offset and dispersion, statistics of the microphone differential path distance can explain variations of performance metrics when steering at targets for immersive or near-field microphone applications.

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Synthesis of nonuniformly spaced arrays using a general nonlinear minimax optimisation method

Cited by 50 publications

References 8 publications

Adaptive near-field beamforming techniques for sound source imaging

Adaptive near-field beamforming techniques for sound source imaging

Sparse Sampling in Array Processing

Geometry descriptors of irregular microphone arrays related to beamforming performance

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