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Measured cross-spectral matrices (CSMs) from a microphone array will in some cases be contaminated by severe incoherent noise signals in the individual channels. A typical example is flow noise generated in the individual microphones when measuring in a wind tunnel. Assuming stationary signals and performing long-time averaging, the contamination will be concentrated on the CSM diagonal. When the CSM is used for traditional frequency-domain beamforming, diagonal removal (DR) will avoid use of the diagonal. DR is effective at suppressing the contamination effects, but it also has some side effects. With other beamforming algorithms and in connection with acoustic holography, however, the diagonal of the CSM is needed. The present paper describes a method for removal of incoherent noise contamination from the CSM diagonal. The method formulates the problem as a semidefinite program, which is a convex optimization problem that can be solved very efficiently and with guaranteed convergence properties. A first numerical study investigates the question, whether the semidefinite program formulation will provide in all cases the desired output. A second numerical study investigates the limitations introduced by off-diagonal noise contributions due to finite averaging time. The results of that study are backed up by results from a practical measurement. [ABSTRACT FROM AUTHOR]

Compressive beamforming has been successfully applied to direction-of-arrival estimation with sensor arrays. The results demonstrated that this technique achieves superior performance when compared with traditional high-resolution beamforming methods. The existing compressive beamforming methods use classical iterative optimization algorithms in their compressive sensing theories. However, the computational complexity of the existing compressive beamforming methods tend to be excessively high, which has limited the use of compressive beamforming in applications with limited computing resources. To address this issue, this paper proposes a fast compressive beamforming method which combines the shift-invariance of the array beam patterns with a fast iterative shrinkage-thresholding algorithm. The evaluation shows that the proposed fast compressive beamforming method successfully reduces the number of floating-point operations by 3 orders of magnitude when compared with the existing methods. In addition, both the simulations and experiments demonstrate that the resolution limit for discerning closely spaced sources of the introduced fast method is comparable to those of the existing compressive beamforming methods, which use classical iterative optimization algorithms. [ABSTRACT FROM AUTHOR]

The first aim of this paper is to give emphasis to the importance of assessing phase information when reconstructing and mapping a sound field. In fact, in acoustic analysis phase distribution is frequently simply either not considered or ignored, even though it can supply very useful information for the understanding of the mapping itself or for further analyses. In this paper a procedure to carry out phase mapping of acoustic sources in beamforming measurement is illustrated. The second aim of this paper is to propose a straightforward iterative optimization algorithm based on the monopole substitution starting from beamforming results. It allows for the reconstruction of sound field without the use of any matrix inversion. Both numerical and experimental validations of the method are presented. Results shown hereafter prove the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Compressive spherical beamforming (CSB) with spherical microphone arrays enjoys high spatial resolution and strong sidelobes suppression at medium to high frequencies. In this paper, the adaptive reweighting homotopy (ARH) algorithm is adapted to CSB, and the proposed method is abbreviated as ARH-CSB, which bypasses a priori signal-to-noise ratio (SNR) estimation. ARH-CSB iteratively solves the CSB model by determining the support set and adaptively searching for weights while estimating the source strength. Simulations and experiments demonstrate that ARH-CSB can effectively identify acoustic sources. Furthermore, compared to CSB solved by ℓ 1 -norm minimization algorithm and iterative reweighted ℓ 1 -norm minimization algorithm, ARH-CSB does not depend on a priori SNR and has better adaptability to low SNR, stronger robustness, higher weak sources quantification accuracy, and faster computation speed. [ABSTRACT FROM AUTHOR]

This paper introduces an eigenvector pruning algorithm for the estimation of the signal-plus-interference eigenspace, required as a preliminary step to subspace beamforming. The proposed method considers large-aperture passive array configurations operating in environments with multiple maneuvering targets in background noise, in which the available data for estimation of sample covariances and eigenvectors are limited. Based on statistical properties of scalar products between deterministic and complex random vectors, this work defines a statistically justified threshold to identify target-related features embedded in the sample eigenvectors, leading to an estimator for the signal-bearing eigenspace. It is shown that data projection into this signal subspace results in sharpening of beamforming outputs corresponding to closely spaced targets and provides better target separation compared to current subspace beamformers. In addition, the proposed threshold gives the user control over the worst-case scenario for the number of false detections by the beamformer. Simulated data are used to quantify the performance of the subspace estimator according to the distance between estimated and true signal subspaces. Beamforming resolution using the proposed method is analyzed with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment. [ABSTRACT FROM AUTHOR]

This letter deals with the problem of differential beamforming with microphone arrays of arbitrary planar geometry. By approximating the beampattern with the Jacobi-Anger expansion, it develops an algorithm that can form any specified frequency-invariant beampattern with a microphone array of any planar geometry as long as the sensors' coordinates are given and the spacing between neighboring sensors is smaller than the smallest wavelength. This method is rather general and it can be used to design differential beamformers with linear, circular, and concentric circular differential microphone arrays as well as differential arrays of arbitrary planar geometry where sensors are placed in any specified positions. [ABSTRACT FROM AUTHOR]

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1◻ mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan. [ABSTRACT FROM AUTHOR]

Classical beamforming algorithms are based on rectilinear propagation of signals that are corrupted by additive noise. However, sound waves propagating in the atmosphere are refracted by temperature and wind gradients and distorted by turbulence and scattering from the ground. These effects can reduce the performance of acoustic systems. To investigate these effects, a simulation capability is used to propagate signals from elevated sources to an array of microphones near the ground. Detection algorithms based on four standard beamforming algorithms, implemented with either no model mismatch error or model mismatch errors generated by atmospheric refraction, are tested. The results are evaluated using the area under the receiver operating characteristic curve statistic. The analysis shows no significant improvement in detection performance when refraction effects are included in the beamforming algorithms over a wide range of frequencies. Any small performance improvements achieved by incorporating refraction effects into the beamforming algorithms are overwhelmed by the negative effects of atmospheric turbulence and rough surface scattering. For the selected scenario, incorporating refraction effects into the beamformers is over-engineering. These results demonstrate some of the limitations of developing new signal processing algorithms or array designs for atmospheric acoustic systems. [ABSTRACT FROM AUTHOR]

The application of the Sliding Frank-Wolfe algorithm to gridless compressive beamforming is investigated for single and multi-snapshot measurements and the estimation of the three-dimensional (3D) position of the sources and their amplitudes. Sources are recovered by solving an infinite dimensional optimization problem, promoting sparsity of the solutions, and avoiding the basis mismatch issue. The algorithm does not impose constraints on the source model or array geometry. A variant of the algorithm is proposed for greedy identification of the sources. The experimental results and Monte Carlo simulations in 3D settings demonstrate the performances of the method and its numerical efficiency compared to the state of the art. [ABSTRACT FROM AUTHOR]

Beamforming using a circular array of hydrophones may be employed for the task of two-dimensional (2D) underwater sound-field visualisation. In this article, a parametric spatial post-filtering method is proposed, which is specifically intended for applications involving large circular arrays and aims to improve the spatial selectivity of traditional beamformers. In essence, the proposed method is a reformulation of the cross-pattern coherence (CroPaC) spatial post-filter, which involves calculating the normalised cross-spectral density between two signals originating from coincident beamformers. The resulting parameter may be used to sharpen another beamformer steered in the same look-direction, while attenuating ambient noise and interferers from other directions. However, while the original 2D version of the algorithm has been demonstrated to work well with second-order circular harmonic input, it becomes increasingly less suitable with increasing input order. Therefore, the proposed reformulation extends the applicability of CroPaC for much higher orders of circular harmonic input. The method is evaluated with simulated data of a 96-channel circular hydrophone array in three different passive sonar scenarios, where the proposed post-filter is shown to improve the spatial selectivity of both delay-and-sum and minimum-variance distortionless response beamformers. [ABSTRACT FROM AUTHOR]

The convex sparse penalty based compressive beamforming technique can achieve robust high resolution in direction-of-arrival (DOA) estimation tasks, but it often leads to an insufficient sparsity-inducing problem due to its convex loose approximation to ideal ℓ 0 nonconvex penalty. On the contrary, the nonconvex sparse penalty can tightly approximate ℓ 0 penalty to effectively enhance DOA estimation accuracy, but it incurs an initialization sensitivity problem due to its multiple local minimas. Leveraging their individual advantages, a minimax-concave penalty (MCP) regularized DOA estimation algorithm is proposed to achieve a maximally sparse level while maintaining the convex property of the overall objective function. Moreover, an accelerated block gradient descent-ascent algorithm with convergence guarantee is developed to rapidly achieve its one optimal point. Simulation results demonstrate that MCP penalty improves DOA estimation accuracy compared with popular sparse compressive beamforming techniques in strong noise scenarios and weak source confirmation. Ocean experimental results also validate that it retains more stable DOA estimation accuracy and incurs less artificial interferences. [ABSTRACT FROM AUTHOR]

An algorithm is developed based on alternating direction method of multipliers to solve the weighted atomic norm minimization in two-dimensional grid-free compressive beamforming. Simulations and experiments are carried out to compare this algorithm with the off-the-shelf interior point method based sdpt3 solver in cvx toolbox. Whether a standard uniform rectangular array or a non-uniform array constructed by a small number of microphones is utilized, this algorithm is feasible and faster. [ABSTRACT FROM AUTHOR]

Compressive beamforming realizes the direction-of-arrival (DOA) estimation and strength quantification of acoustic sources by solving an underdetermined system of equations relating microphone pressures to a source distribution via compressive sensing. The conventional method assumes DOAs of sources to lie on a grid. Its performance degrades due to basis mismatch when the assumption is not satisfied. To overcome this limitation for the measurement with plane microphone arrays, a two-dimensional grid-free compressive beamforming is developed. First, a continuum based atomic norm minimization is defined to denoise the measured pressure and thus obtain the pressure from sources. Next, a positive semidefinite programming is formulated to approximate the atomic norm minimization. Subsequently, a reasonably fast algorithm based on alternating direction method of multipliers is presented to solve the positive semidefinite programming. Finally, the matrix enhancement and matrix pencil method is introduced to process the obtained pressure and reconstruct the source distribution. Both simulations and experiments demonstrate that under certain conditions, the grid-free compressive beamforming can provide high-resolution and low-contamination imaging, allowing accurate and fast estimation of two-dimensional DOAs and quantification of source strengths, even with non-uniform arrays and noisy measurements. [ABSTRACT FROM AUTHOR]

Multiple raypaths propagation is caused by reflection and refraction at the surface and the bottom of the water column. In this study, an active wideband higher-order separation is proposed, which enables the separation of raypaths interrupted by colored noise (as traditionally found in ocean environments) in the angle-vs-time domain. A comparative study shows that the proposed algorithm achieves a more accurate separation than other algorithms. Moreover, with the proposed approach, it is not necessary to assume that the number of sensors is larger than that of the sources. Furthermore, numerical results validate the noise suppression property of the proposed method. [ABSTRACT FROM AUTHOR]

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A geoacoustic inversion scheme based on a double beamforming algorithm in shallow water is proposed and tested. Double beamforming allows identification of multi-reverberated eigenrays propagating between two vertical transducer arrays according to their emission and reception angles and arrival times. Analysis of eigenray intensities yields the bottom reflection coefficient as a function of angle of incidence. By fitting the experimental reflection coefficient with a theoretical prediction, values of the acoustic parameters of the waveguide bottom can be extracted. The procedure was initially tested in a small-scale tank experiment for a waveguide with a Plexiglas bottom. Inversion results for the speed of shear waves in Plexiglas are in good agreement with the table values. A similar analysis was applied to data collected during an at-sea experiment in shallow coastal waters of the Mediterranean. Bottom reflection coefficient was fitted with the theory in which bottom sediments are modeled as a multi-layered system. Retrieved bottom parameters are in quantitative agreement with those determined from a prior inversion scheme performed in the same area. The present study confirms the interest in processing source–receiver array data through the double beamforming algorithm, and indicates the potential for application of eigenray intensity analysis to geoacoustic inversion problems. [ABSTRACT FROM AUTHOR]

A method is introduced where blind source separation of acoustical sources is combined with spatial processing to remove non-Gaussian, broadband interferers from space-time displays such as bearing track recorder displays. This differs from most standard techniques such as generalized sidelobe cancellers in that the separation of signals is not done spatially. The algorithm performance is compared to adaptive beamforming techniques such as minimum variance distortionless response beamforming. Simulations and experiments using two acoustic sources were used to verify the performance of the algorithm. Simulations were also used to determine the effectiveness of the algorithm under various signal to interference, signal to noise, and array geometry conditions. A voice activity detection algorithm was used to benchmark the performance of the source isolation. [ABSTRACT FROM AUTHOR]

Acoustic remote sensing of the oceans requires a detailed understanding of the acoustic forward problem. The results of a shallow-water transmission experiment between a vertical array of sources and a vertical array of receivers are reported. The source array is used to provide additional degrees of freedom to isolate and track raylike arrivals by beamforming over both source and receiver arrays. The coordinated source-receiver array processing procedure is presented and its effectiveness in an example of tracking raylike arrivals in a fluctuating ocean environment is shown. Many of these arrivals can be tracked over an hour or more and show slowly varying amplitude and phase. The use of a double-beamforming algorithm lays the foundation for shallow-water acoustic remote sensing using travel time and source and receive angles of selected eigenrays. [ABSTRACT FROM AUTHOR]