Conventional Beamforming Methods Research Articles

A Low-Complexity Robust Beamforming Using Diagonal Unloading for Acoustic Source Localization

In sensor array beamforming methods, a class of algorithms commonly used to estimate the position of a radiating source, the diagonal loading of the beamformer covariance matrix is generally used to improve computational accuracy and localization robustness. This paper proposes a diagonal unloading (DU) method which extends the conventional response power beamforming method by imposing an additional constraint to the covariance matrix of the array output vector. The regularization is obtained by subtracting a given amount of white noise from the main diagonal of the covariance matrix. Specifically, the DU beamformer aims at subtracting the signal subspace from the noisy signal space and it is computed by constraining the regularized covariance matrix to be negative definite. It is hence a data-dependent covariance matrix conditioning method. We show how to calculate precisely the unloading parameter, and we present an eigenvalue analysis for comparing the proposed DU beamforming, the minimum variance distortionless response (MVDR) filter and the multiple signal classification (MUSIC) method. Theoretical analysis and experiments with acoustic sources demonstrate that the DU beamformer localization performance is comparable to that of MVDR and MUSIC. Since the DU beamformer computational cost is comparable to that of a conventional beamformer, the proposed method can be attractive in array processing due to its simplicity, effectiveness and computational efficiency.

Recovery of the Complete Data Set From Focused Transmit Beams.

The focused transmit beam is a standard tool for clinical ultrasound scanning, concentrating energy from a number of array elements toward an imaging target. However, above and below the transmit focus, much of the energy in the beam is spread in a broadened main lobe and long off-axis tails that are ignored by conventional beamforming methods. This paper proposes a method to decompose a set of focused transmit beams into their constituent components-diverging waves from individual array elements. The recovery of this complete data set enables synthetic transmit focusing at all points in the field of view without beam shape or focal depth artifacts commonly associated with virtual source synthetic aperture beamforming. An efficient frequency-domain linear decoding implementation is introduced. The principles of the method are demonstrated both in transmit field simulations and experimental imaging. At depth, up to a 9.6-dB improvement in electronic signal-to-noise ratio and 8.9-dB improvement in contrast were observed in comparison with conventional dynamic receive beamforming. The proposed method is broadly applicable to existing scan sequences and requires only channel data for processing.

Investigation of Low-Sidelobe Beampattern Controlling Methods for Acoustic Transmitting Array of Underwater Vehicles

Abstract In underwater unmanned vehicles, complex acoustic transducer arrays are always used to transmitting sound waves to detect and position underwater targets. Two methods of obtaining low-sidelobe transmitting beampatterns for acoustic transmitting arrays of underwater vehicles are investigated. The first method is the boundary element model optimization method which used the boundary element theory together with the optimization method to calculate the driving voltage weighting vector of the array. The second method is the measured receiving array manifold vector optimization method which used the measured receiving array manifold vectors and optimization method to calculate the weighting vector. Both methods can take into account the baffle effect and mutual interactions among elements of complex acoustic arrays. Computer simulation together with experiments are carried out for typical complex arrays. The results agree well and show that the two methods are both able to obtain a lower sidelobe transmitting beampattern than the conventional beamforming method, and the source level for each transmitting beam is maximized in constraint of the maximum driving voltage of array elements being constant. The effect of the second method performs even better than that of the first method, which is more suitable for practical application. The methods are very useful for the improvement of detecting and positioning capability of underwater unmanned vehicles.

A dual mode hemispherical sparse array for B-mode skull localization and passive acoustic mapping within a clinical MRI guided focused ultrasound platform

Previous work has demonstrated that passive acoustic imaging may be used alongside MRI for monitoring of focused ultrasound therapy. However, current implementations have generally made use of either linear arrays originally designed for diagnostic imaging or custom narrowband arrays specific to in-house therapeutic transducer designs, neither of which is fully compatible with clinical MR-guided focused ultrasound devices. Here we have designed an array which is suitable for use within an FDA-approved MR-guided transcranial focused ultrasound device, within the bore of a 3 Tesla clinical MRI scanner. The array is constructed from 5 × 0.4mm piezoceramic disc elements arranged in pseudorandom fashion on a low profile laser-cut acrylic frame designed to fit between the therapeutic elements of a 230 kHz InSightec ExAblate 4000 transducer. By exploiting thickness and radial resonance modes of the piezo discs the array is capable of both B-mode imaging at 5 MHz for skull localization, as well as passive reception at the second harmonic of the therapy array for mapping of acoustic sources such as emissions from cavitation. The strengths and limitations of the system for passive acoustic imaging during in vivo experiments will be discussed, utilizing robust and conventional time and frequency domain beamforming methods.

Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid

Phased microphone arrays are used widely in the applications for acoustic source localization. Deconvolution approaches such as DAMAS successfully overcome the spatial resolution limit of the conventional delay-and-sum (DAS) beamforming method. However deconvolution approaches require high computational effort compared to conventional DAS beamforming method. This paper presents a novel method that serves to improve the efficiency of DAMAS via wavelet compression computational grid rather than via optimizing DAMAS algorithm. In this method, the efficiency of DAMAS increases with compression ratio. This method can thus save lots of run time in industrial applications for sound source localization, particularly when sound sources are just located in a small extent compared with scanning plane and a band of angular frequency needs to be calculated. In addition, this method largely retains the spatial resolution of DAMAS on original computational grid, although with a minor deficiency that the occurrence probability of aliasing increases slightly for complicated sound source.

Spatial Spectrum Sparse Reconstruction for Maneuvering Towed Array

The port-starboard ambiguity in the conventional single towed linear array sonar is one of the most deceiving obstacles for the spatial spectrum estimation. In order to improve the performance of target detection and Direction of Arrival (DOA) estimation, a spatial spectrum sparse representation model is established using multiple beam-space measurements. The forward-backward operator splitting method is adopted for solving the sparse optimization problem. Simulation results demonstrate that, compared with Conventional Beam Forming (CBF) method, the proposed method has evident advantages in ambiguity suppression and estimation performance.

Eigenspace-based beamformer using oblique signal subspace projection for ultrasound plane-wave imaging.

BackgroundThe Eigenspace-based beamformers, by orthogonal projection of signal subspace, can remove a large part of the noise, and provide better imaging contrast upon the minimum variance beamformer. However, wrong estimate of signal and noise component may bring dark-spot artifacts and distort the signal intensity. The signal component and noise and interference components are considered uncorrelated in conventional eigenspace-based beamforming methods. In ultrasound imaging, however, signal and noise are highly correlated. Therefore, the oblique projection instead of orthogonal projection should be taken into account in the denoising procedure of eigenspace-based beamforming algorithm.MethodsIn this paper, we propose a novel eigenspace-based beamformer based on the oblique subspace projection that allows for consideration of the signal and noise correlation. Signal-to-interference-pulse-noise ratio and an eigen-decomposing scheme are investigated to propose a new signal and noise subspaces identification. To calculate the beamformer weights, the minimum variance weight vector is projected onto the signal subspace along the noise subspace via an oblique projection matrix.ResultsWe have assessed the performance of proposed beamformer by using both simulated software and real data from Verasonics system. The results have exhibited the improved imaging qualities of the proposed beamformer in terms of imaging resolution, speckle preservation, imaging contrast, and dynamic range.ConclusionsResults have shown that, in ultrasound imaging, oblique projection is more sensible and effective than orthogonal subspace projection. Better signal and speckle preservation could be obtained by oblique projection compare to orthogonal projection. Also shadowing artifacts around the hyperechoic targets have been eliminated. Implementation the new subspace identification has enhanced the imaging resolution of the minimum variance beamformer due to the increasing the signal power in direction of arrival. Also it has offered better sidelobe suppression and a higher dynamic range.

Fast Broadband Beamforming Using Nonuniform Fast Fourier Transform for Underwater Real-Time 3-D Acoustical Imaging

A broadband 2-D array is necessary for the application of underwater real-time 3-D high-resolution acoustical imaging. However, the number of sensors and beams is so large that the computational load is overly high for generating a 3-D image in real time, when using the conventional broadband beamforming methods. Therefore, a fast broadband beamforming method is needed. In this paper, a fast broadband frequency-domain beamforming method based on nonuniform fast Fourier transform is proposed for underwater real-time 3-D acoustical imaging. The computational load of the proposed method is one or two orders of magnitude lower than that by the conventional frequency-domain direct method. Compared with the conventional time-domain delay-and-sum method, the computational load required by the proposed method will be lowered by approximately three orders of magnitude. Moreover, unlike the chirp zeta transform (CZT) beamforming method which is only suitable for equispaced 2-D arrays or sparse arrays thinned from equispaced 2-D arrays, the proposed method can be applied to arbitrary 2-D arrays. Even for the arrays available for the CZT method, the computational load needed by using the proposed method is still lower than that needed by using the CZT method in most cases.

Subarray compressive beamforming for midfrequency active sonar on a cylindrical array

Subarray compressive beamforming is adapted to a cylindrical array for midfrequency active sonar detection of low-Doppler targets in reverberant environments. Low Doppler targets are difficult to detect due to spreading caused by side-lobes and the width of the main lobe. Compressive beamforming and, in particular, subarray compressive beamforming will be shown to drastically mitigate Doppler spreading and interference from static objects. A straightforward application of compressive beamforming to simulated data results in nearly perfect suppression of spread due to sidelobes and good isolation of low Doppler targets from the zero Doppler ridge. Artificially subdividing the array allows for multiple fully resolved sparse estimates of the incident sound field. The set of estimates from the set of subarrays may be combined to form a single sparse estimate, often fully suppressing zero Doppler clutter. Performance of both compressive beamforming and subarray compressive beamforming will be demonstrated using simulated data having realistic Doppler spreading due to multipath and environmental sources of Doppler spreading. Performance will be compared to conventional beamforming methods on the cylindrical array. In addition to practical applications, some theoretical challenges with adaptation to the cylindrical array will be discussed. [Work supported by a NAVSEA Phase I SBIR award.]

A Millimeter-Wave WPAN Adaptive Phased Array Control Method Using Low-Frequency Part of Signal for Self-Directed System

In this paper, we propose a self-directed adaptive phased array control method using the low-frequency part of the signal for a millimeter-wave wireless personal area network, which can reduce the hardware requirement and power consumption compared with those of the conventional digital beamforming method. The signal from each element antenna of the antenna array is phase shifted and down converted to the baseband and then divided into two paths. The first paths are low-pass filtered to extract the low-frequency part of the signals for beamforming control. The second paths from all antennas are combined in-phase in an analog domain and then sampled by two high-speed A/D converters for demodulation. A beamforming algorithm using the sampled low-frequency part of the signal is also proposed. The beamforming calculation time is established as a function of the signal-to-noise ratio, the bandwidth of the low-frequency part, and the required phase control accuracy. The calculated values match the measured results. Using the IEEE 802.15.3c specifications with an eight-element array antenna, the calculation time is less than ${\hbox{5}}~\mu{\hbox{s}}$ for initial beam establishment and less than ${\hbox{30}}~\mu{\hbox{s}}$ for beam tracking. Therefore, high-speed beamforming is possible while reducing the power consumption.

Three-dimensional beamforming of dipolar aeroacoustic sources

This paper outlines and compares four beamforming algorithms for accurately localising acoustic dipole sources in a three-dimensional domain, such as noise sources produced by flow–body interaction. These algorithms include conventional cross-spectral beamforming, conventional beamforming with deconvolution via CLEAN-SC, ‘multiplicative’ cross-spectral beamforming and multiplicative beamforming with CLEAN-SC. The latter two algorithms are novel to the field of aeroacoustics and rely on the mutual cancellation of spatially incoherent sources between orthogonally aligned microphone arrays to improve the quality of the source map. The algorithms were used on both synthetic and experimental data. By comparing the performance of each algorithm in terms of source localisation accuracy, source strength estimation and resolution, it was found that conventional beamforming with CLEAN-SC is the preferred method for beamforming aeroacoustic sources in three dimensions, albeit at a higher computational cost than the other three. The results also showed that multiplicative beamforming methods give source maps that are more interpretable than conventional cross-spectral beamforming methods at no extra computational expense.

Distant speech separation using predicted time–frequency masks from spatial features

Speech separation algorithms are faced with a difficult task of producing high degree of separation without containing unwanted artifacts. The time–frequency (T–F) masking technique applies a real-valued (or binary) mask on top of the signal’s spectrum to filter out unwanted components. The practical difficulty lies in the mask estimation. Often, using efficient masks engineered for separation performance leads to presence of unwanted musical noise artifacts in the separated signal. This lowers the perceptual quality and intelligibility of the output.Microphone arrays have been long studied for processing of distant speech. This work uses a feed-forward neural network for mapping microphone array’s spatial features into a T–F mask. Wiener filter is used as a desired mask for training the neural network using speech examples in simulated setting. The T–F masks predicted by the neural network are combined to obtain an enhanced separation mask that exploits the information regarding interference between all sources. The final mask is applied to the delay-and-sum beamformer (DSB) output.The algorithm’s objective separation capability in conjunction with the separated speech intelligibility is tested with recorded speech from distant talkers in two rooms from two distances. The results show improvement in instrumental measure for intelligibility and frequency-weighted SNR over complex-valued non-negative matrix factorization (CNMF) source separation approach, spatial sound source separation, and conventional beamforming methods such as the DSB and minimum variance distortionless response (MVDR).

A sponge-layer damping technique for aeroacoustic Time-Reversal

This paper presents the underlying theory, associated mathematical modelling and analysis of a sponge-layer damping technique, termed the Time-Reversal-Sponge-Layer (TRSL), that significantly improves the performance of aeroacoustic Time-Reversal (TR). The TR technique requires the use of multiple Line Arrays (LAs) in a Time-Reversal Mirror (TRM) to accurately predict the source location and its characteristics. However, it is shown that when using multiple LAs, the interference between the opposite propagating fluxes near the LA boundaries results in the formation of spurious local maxima regions throughout the computational domain, thereby reducing the capacity of TR to resolve acoustic sources. The novel TRSL technique proposed in this work minimises this unwanted interference by damping the flux normally incident on a LA boundary and is implemented using the Pseudo-Characteristic Formulation (PCF) of the two-dimensional Linearised Euler Equations (LEE). The performance of TRSL is assessed by simulating a number of test cases such as an idealised time-harmonic monopole, dipole and lateral quadrupole sources as well as multiple (two) dipole sources of different strengths located in a nonuniform mean shear flow. The use of TRSL suppresses the formation of spurious maxima and significantly improves the source map, thereby demonstrating the effectiveness of this damping technique. The performance of TRSL is compared with two other methods: a TR superposition technique and Conventional Beamforming (CB). The TR superposition technique prevents the flux interference problem near the LA boundaries by superposing the instantaneous time-reversed acoustic pressure fields computed from individual LAs. The source map obtained using the superposition technique was found to be identical to that obtained using the TRSL damping technique, however, the computational cost was much higher. A comparison with CB indicated that although CB accurately predicts the aeroacoustic source location, the relative magnitudes of the side-lobes in the CB source map(s) are substantially higher in comparison to the corresponding TR source map(s). It was also shown that TR with use of the TRSL is better suited for resolving multiple coherent sources in a domain than the CB method, especially when one source is much weaker than other sources in the domain.

Design of an Adaptive Wideband Beamforming Algorithm for Conformal Arrays

Wideband conformal arrays are increasingly used in modern communication systems due to their flexibility in attaching to arbitrary surfaces. However, designing such arrays has been limited to pattern synthesis techniques because of the complexity of their geometrical analysis and the different contribution of each sensor in the array response. These techniques are basically non-adaptive data independent methods and are not suitable for dynamic environments. In this letter, the conventional Frost space-time wideband beamforming method and its associated Linearly Constrained Minimum Variance (LCMV) algorithm are employed to develop an adaptive wideband beamforming method for an arbitrarily shaped conformal array. The constraint matrix used in the LCMV algorithm is modified to include the pattern of each individual sensor, thereby incorporating the element responses into the weight adjustment algorithm. A set of angle-frequency constraints are used in the proposed LCMV formulation to achieve the desired frequency-invariant beampattern. It is shown by a comprehensive example of a typical conical array that the desired beampattern can be obtained in an adaptive manner for a time-varying target.

Fourth-Order Cumulants Beamforming for Direction of Arrival Estimation

With the rapid development of DOA (direction-of-arrival) estimation methods, the existing beamforming technologies still have the problem of a way to balance high spatial resolution and robust performance. The aperture extension characteristics of fourth-order cumulants is used to raise the spatial resolution of the array in this paper. By using the structure of minimum redundancy array, the calculation of the fourth-order cumulants matrix can be reduced. Simulations of the properties of fourth–order cumulants beamforming method are analyzed in this paper, including the resolution analysis, multi-sources analysis and error analysis. Simulation results show that the fourth-order cumulants beamforming method has a higher spatial resolution than the conventional beamforming method without increasing the number of the array sensors.

A robust super-resolution approach with sparsity constraint in acoustic imaging

Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests.

Experimental evaluation of flow-induced noise in level flight of the pigeon (Columba livia)

The experimental method employed in an anechoic wind tunnel to characterize flow-induced noise of the pigeon (Columba livia) during level flight is described in this letter. A live pigeon was managed to maintain a steady level flight at the wind tunnel test flow of 15 m/s. A microphone array was fabricated, and the conventional beamforming method was then adopted to yield the corresponding narrowband acoustic images and broadband sound pressure spectral results. The results justified the experimental method developed in this work. It can be seen that the flight noise of the pigeon is mostly from the wing tips. In addition, the spectral waveform of the pigeon flight suggests a slope of -20 dB/dec between 500 Hz and 5 kHz.

Enhanced spatial analysis of room acoustics using acoustic multiple-input multiple-output systems

Standard acoustic measurements in enclosures typically employ single-input single-output (SISO) acoustic systems. The parameters obtained from these measurements describe features of energy decay and do not characterize spatial attributes of the enclosure. Directional analysis of enclosures became popular with the introduction of microphone and loudspeaker arrays. In particular, spherical arrays have been shown to be highly beneficial for spatial analysis. Spherical microphone arrays facilitate the estimation of the arrival direction of the direct and reflected sound, while the use of both loudspeaker and microphones arrays can support the estimation of both radiation and arrival directions, with the application of conventional beamforming methods. However, when several reflections are attributed to the same time bin in a discrete impulse response, reflection paths may not be uniquely determined by existing beamforming techniques. We present a new method to uniquely determine source and receiver directions for multiple reflections when time separation is unfeasible. The paper presents the formulation of the proposed method, also showing a simulation study to demonstrate the performance of the proposed method.

Two-stage through-the-wall radar image formation using compressive sensing

We introduce a robust image-formation approach for through-the-wall radar imaging (TWRI). The proposed approach consists of two stages involving compressive sensing (CS) followed by delay-and-sum (DS) beamforming. In the first stage, CS is used to reconstruct a complete set of measurements from a small subset collected with a reduced number of transceivers and frequencies. DS beamforming is then applied to form the image using the reconstructed measurements. To promote sparsity of the CS solution, an overcomplete Gabor dictionary is employed to sparsely represent the imaged scene. The new approach requires far fewer measurement samples than the conventional DS beamforming and CS-based TWRI methods to reconstruct a high-quality image of the scene. Experimental results based on simulated and real data demonstrate the effectiveness and robustness of the proposed two-stage image formation technique, especially when the measurement set is drastically reduced.

A PARETO ELITE SELECTION GENETIC ALGORITHM FOR RANDOM ANTENNA ARRAY BEAMFORMING WITH LOW SIDELOBE LEVEL

Random antenna array (RAA) that uses the conventional beamforming method produces a poor beam pattern with high sidelobe level. This greatly reduces the performance and the e-ciency of the antenna. The use of Genetic Algorithm (GA) to flnd the best positions for the antenna elements in RAA to lower the sidelobes has been widely researched. However, there has been no solution proposed for the reduction of sidelobes when the user has no autonomy over the position of the radiating elements, for instance in cases such as emergency communications. This paper proposes a novel Pareto Elite Selection Genetic Algorithm (PESGA) optimization method to reduce the sidelobes in an RAA that has flxed elements' position. The proposed method uses a single fltness function (peak sidelobe level) for parent selection while an additional function (number of sidelobes above a threshold level) is introduced to select the elitist in every generation via Pareto Front (PF) selection. Results indicate that the proposed PESGA method is best used for scenarios where the array size is small. In such cases, the proposed method provides much reduced sidelobe compared to the conventional RAA beamforming method and up to 200% improvements in terms of mainlobe to peak sidelobe ratio compared to GA weight optimized beamforming method.