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Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks. [ABSTRACT FROM AUTHOR]

This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real static human-robot interaction (HRI) data, the proposed combination of BSS with the minimum-variance distortionless response beamformer provides a greater signal-to-noise ratio (SNR) than previous parallel and cascade systems that combine BSS and beamforming. In the difficult-to-model HRI dynamic environment, the system provides a SNR gain that was 2.8 dB greater than the results obtained with the cascade combination, where the parallel combination is infeasible. [ABSTRACT FROM AUTHOR]

This paper presents an alternative energy function for global optimization (GO) beamforming, tailored to acoustic broadband sources. Given that properties, such as the source location, multipole rotation, or flow conditions, are parameterized over the frequency, a cross spectral matrix-fitting can be performed for all frequencies at once. A numerical analysis shows that the nonlinear energy function for the standard GO problem is equivalent to the source's point spread function (PSF) and contains local minima at the locations of the grating lobes and side lobes. The energy function is improved with the proposed broadband energy, as it averages the PSF. Further, it simplifies the process of identifying sources and reconstructing their spectra from the results. The paper shows that the method is superior on synthetic monopoles compared to standard GO and CLEAN based on source coherence (CLEAN-SC). For real-world data, the results of the proposed method and CLEAN-SC are similar, and outperform standard GO. The main difference is that source assumption violations cause noisy maps for CLEAN-SC and cause wrong spectral estimations of the proposed method. By using reasonable initial values, the GO problem reduces to a local optimization problem with similar results. Further, the proposed method is able to identify synthetic multipoles with different pole amplitudes and unknown pole rotations. [ABSTRACT FROM AUTHOR]

In passive sonar systems, deviations from an ideal linear configuration can significantly impair the beamforming performance of towed hydrophone arrays. This paper presents a method aimed at improving the underwater acoustic signals in the presence of array distortion. The method is centered on a high-order time-delay difference estimation technique utilizing time-frequency autofocus. Initially, a detailed signal model is established that captures the distinctive characteristics of distorted arrays. Subsequently, an algorithm is introduced for high-order time-delay difference estimation to enhance signal fidelity by leveraging phase information within narrowband components originating from incidental acoustic sources. Additionally, a quality metric to evaluate these components is introduced, facilitating the practical implementation of the method. The effectiveness of the proposed approach is validated through both simulation and experimental results, demonstrating its superiority over existing techniques. Importantly, this method does not require prior knowledge of the distortion pattern, making it adaptable to various non-linear array configurations. [ABSTRACT FROM AUTHOR]

In this paper, an ultrasound beamforming method for plane wave (PW) imaging based on modified sidelobe blanking (MSLB) is proposed to improve image resolution and contrast ratio (CR). In this framework, PWs from various angles were designed to create main and auxiliary beamformer signals. Specifically, the PW signals from all angles were first coherently combined to serve as the main beamformer output signals. To prevent excessive clutter and noise, output signals in the main beamformer were weighted by the generalized coherence factor. Subsequently, the PW signals were split into positive and negative angles to perform a subtraction, creating the auxiliary beamformer. Finally, signals in the main beamformer were compared with the signals in the auxiliary beamformer point by point to further eliminate the noises and clutters. Compared with the delay and sum, full width at half maximum of the MSLB for point targets was reduced by an average of 54.17% and 51.65% in simulations and experiments, respectively; and the corresponding CR was improved by 55.38% and 18.40% on average. The MSLB method provided better imaging quality in human carotid arteries. In conclusion, the proposed method can effectively improve image resolution and CR with low computational complexity. [ABSTRACT FROM AUTHOR]

Conventional beamforming (CBF) is a commonly employed approach for detecting and estimating the direction-of-arrival (DOA) of acoustic signals in underwater environments. However, CBF becomes ambiguous due to spatial aliasing when the received signal's half wavelength is smaller than the array spacing. Frequency-difference beamforming (FDB) allows for processing data in the lower frequency Δ f without encountering spatial aliasing by utilizing the product of array data at frequency f with its complex conjugate at frequency f + Δ f. However, lower frequency results in a wider mainlobe, which can lead to poorer DOA performance for short arrays. In this paper, a fourth-order cumulants FDB method and a conjugate augmented FDB method are proposed to extend an M -element uniform linear array to 2 M − 1 and 4 M − 3 elements. The proposed methods generate narrower beams and lower sidelobe levels than the original FDB for short arrays with large spacing. And by setting the signal subspace dimension reasonably, the proposed methods can improve the weak target detection ability under strong interference compared with FDB. Last, we verify the excellent performance of the proposed methods through simulations and experimental data. [ABSTRACT FROM AUTHOR]

Counter-rotating rotor configurations are considered more efficient than their single rotor counterparts. Consequently, the coaxially aligned rotors have appeared in the fixed-wing aircraft sector and are appearing in the quickly developing unmanned aerial vehicle sector, where they are expected to play a significant role, especially for long haul and heavy load configurations. As their noise levels have proven to be rather significant, the localization and reduction of the noise of such counter-rotating blade sets is a relevant topic of interest. One of the dominant contributors to counter-rotating rotor noise is interaction tones. Interaction tones appear at combinations of the harmonics of the blade passing frequencies of the two rotors and are significant throughout the spectra. In this paper, an automated method is presented that analyzes an entire series of beamforming noise source maps using principal component analysis-based methods in order to identify the dominant noise generation mechanisms in frequency bins that are associated with interaction tones. The processing technique is presented herein through the investigation of counter-rotating open rotor datasets developed for a fixed-wing aircraft configuration. With the proposed method, an objective mean has been provided for separating apart contributions from various noise sources, which can be automated, making the processing and investigation of large sets of measurement data rather quick and easy. The method has been developed such that the results of the analysis are easy to comprehend even without specialized prior knowledge in the area of counter-rotating rotor noise. [ABSTRACT FROM AUTHOR]

Source depth estimation is an important yet very difficult task for passive sonars, especially for horizontal linear arrays (HLAs). This paper proposes an efficient two-step depth estimation scheme using narrowband and broadband constructive and deconstructive striation patterns due to interference between the direct (D) and sea surface reflected (SR) arrivals at an HLA on the bottom of deep water. First, the horizontal source-array ranges are derived from triangulation results of solid angle estimates by subarray beamforming. The applicable areas of the method in deep water are investigated through Mento Carlo simulations, assuming different subarray partitioning ways of a given HLA aperture. Second, cost functions are built to match the measured beam intensity striations with modeled ones. To mitigate the spatial smoothing effect of the beam intensity striations during beamforming, a criterion of the largest subarray aperture is established, and a computationally efficient way is presented to model the replicas by the D-SR time delay templates at a single element of the array calculated by ray theory. The performance degradation due to limited source range spans, the distortion of the beam intensity striations, and range estimation errors has been analyzed. Two experimental datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid neural beamformer for multi-channel speech enhancement, which comprises three stages, i.e., beamforming, post-filtering, and distortion compensation, called TriU-Net. The TriU-Net first estimates a set of masks to be used within a minimum variance distortionless response beamformer. A deep neural network (DNN)-based post-filter is then utilized to suppress the residual noise. Finally, a DNN-based distortion compensator is followed to further improve speech quality. To characterize the long-range temporal dependencies more efficiently, a network topology, gated convolutional attention network, is proposed and utilized in the TriU-Net. The advantage of the proposed model is that the speech distortion compensation is explicitly considered, yielding higher speech quality and intelligibility. The proposed model achieved an average 2.854 wb-PESQ score and 92.57% ESTOI on the CHiME-3 dataset. In addition, extensive experiments conducted on the synthetic data and real recordings confirm the effectiveness of the proposed method in noisy reverberant environments. [ABSTRACT FROM AUTHOR]

Beamforming results depend on the spatial resolution of the microphone array used, which may lead to sources close to each other being considered as one. Deconvolution methods that consider all directions simultaneously, such as DAMAS, produce better results in these situations. However, they have a high computational cost, often lack sufficient speed to be used in real-time applications, and have limited accuracy at lower frequencies. This paper introduces a hybrid method to perform deconvolution using a neural network that can improve the speed of deconvolution on high-resolution grids by more than 2 orders of magnitude, while also generating sparser maps without sacrificing accuracy compared to the compressed DAMAS method. [ABSTRACT FROM AUTHOR]

Sound projection is a method of using highly focused sources to project sound onto reflective surfaces to create the impression of surround sound, which can be realized via beamforming using loudspeaker arrays. Generally, these devices are mounted near a surface with a finite acoustic impedance, such as a TV accent wall. The beamforming performance degrades in this half-space with an impedance boundary when the filters are designed using transfer functions measured in the free field. This paper proposes a model for predicting the beamforming performance in a half-space with an impedance boundary in terms of the transfer functions in the free field. A mathematical model is presented, and the results are compared with practical measurements for a 16-driver line array in real-use environments. The results show that reflections from the boundary lead to "dark bands" in the beam, causing the beam to deviate from the designed direction. Using filters designed with transfer functions calculated by the proposed model yields improved beamforming performance. [ABSTRACT FROM AUTHOR]

In deep water, multipath time delays or frequency-domain interference periods of the acoustic intensity combined with multipath arrival angles are typically used for source localization. However, depth estimate is hard to achieve for a narrowband source at a remote part of the direct arrival zone as the required bandwidth increases with the source range. In this paper, a passive source localization method with a vertical line array, suitable for both broadband and narrowband sources, is proposed. Based on the variation trends of multipath angles with source range and depth, source localization is achieved by only matching the measured angles of the direct path and surface-reflected path with model-based values of a predefined grid of potential source locations. Considering the angle resolution limited by the array aperture and the presence of coherent multipath, sparse Bayesian learning is used and compared with the conventional beamforming and the minimum-variance distortionless-response beamforming to resolve and estimate the multipath angles. Simulations and experimental data of explosive sources collected by a vertical line array in the South China Sea are carried out to illustrate the method and demonstrate the performance. [ABSTRACT FROM AUTHOR]

Sparse arrays reduce the number of sensors required to achieve a specific angular resolution by using sensor spacing greater than the half-wavelength. These undersampled sparse arrays require processing algorithms to eliminate aliasing ambiguities. Thinned arrays are sparse arrays whose sensor positions lie on an underlying equally spaced grid. Using data from a shallow water passive sonar experiment, this paper investigates two thinned array geometries (coprime and nested) along with two processing algorithms (multiplicative and min). Coprime and nested arrays consist of two interleaved Uniform Line Arrays (ULAs) where one or both of the ULAs are undersampled. Multiplicative and min processors combine the outputs of the conventionally-beamformed subarrays to estimate the spatial spectrum. While these nonlinear processors can suppress aliasing, they are often plagued by high sidelobes and cross term interference. This paper presents sparse array designs for a shallow waveguide that require 33% fewer sensors than a fully-sampled ULA and provide significant sidelobe attenuation. Experimental data analysis reveals that cross term interference dominates the spectral estimates for the coprime and nested multiplicative processors and the coprime min processor. The nested min processor outperforms its sparse counterparts due to its ability to contend with coherent multipath in the environment. [ABSTRACT FROM AUTHOR]

By employing a tiny frequency offset across the array elements, a frequency diverse array (FDA) produces a beampattern with the property of being range-angle-dependent. However, the beampattern of a traditional FDA has many maxima at undesired range-angle couples, which is unfavorable for target localization and jamming suppression. In this paper, a novel FDA scheme with Hamming interelement spacing, namely, the HIS-FDA, is proposed. By adopting Hamming window weighted interelement spacing and frequency offsets optimized by the genetic algorithm, the HIS-FDA produces a dot-shaped well-performed beampattern, which has superior performance over the existing FDA systems, especially in the angle dimension. The multiple input multiple output technique and multiple matched filters are employed at the receiver to solve the time-varying problem. A new multicarrier (MC) technique termed the sinusoidal MC approach is also presented to improve the beampattern performance, especially in the range dimension. Our proposed scheme outperforms the existing FDA systems in terms of the mainlobe width in the range and angle profiles, the sidelobe level, and the range-angle spatial region of the mainlobe. Comparison results are provided to validate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian estimation method for sequential direction finding. The proposed method estimates the number of directions of arrivals (DOAs) and their DOAs performing operations on the factor graph. The graph represents a statistical model for sequential beamforming. At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model. Variational Bayesian inference then updates the number of DOAs and their DOAs. The method promotes sparse solutions through a Bernoulli-Gaussian amplitude model, is gridless, and provides marginal posterior pdfs from which DOA estimates and their uncertainties can be extracted. Compared to nonsequential approaches, the method can reduce DOA estimation errors in scenarios involving multiple time steps and time-varying DOAs. Simulation results demonstrate performance improvements compared to state-of-the-art methods. The proposed method is evaluated using ocean acoustic experimental data. [ABSTRACT FROM AUTHOR]

For rotating sound source localization, there are mainly two types of frequency-domain beamforming methods, the virtual rotating array (VRA) method and mode composition beamforming (MCB). Compared with the VRA method, MCB overcomes the constraint of microphone array configuration and avoids error introduced by interpolation. In order to obtain the MCB result, modal transfer functions, which are the modal coefficients in the analytical solution of the sound field of rotating source, should be pre-calculated. However, the computation of modal transfer functions takes a long time, especially under a large number of considered frequencies. Therefore, the computational efficiency of MCB is limited by the computation of modal transfer functions. In this paper, an expression of modal transfer functions at multiple considered frequencies is derived first. According to this expression, intermediate variables independent of frequency are computed in a fast way, then modal transfer functions at each considered frequency are calculated through these intermediate variables. This expression is successfully applied to improve the computational efficiency of MCB for rotating broadband source localization. [ABSTRACT FROM AUTHOR]

Two figure-8 sensors, differently oriented but collocated, can facilitate azimuth- elevation two-dimensional beamforming that is invariant of the frequency spectrum of the incident signal. For such a pair of identical figure-8 sensors of any arbitrary directivity-order, their spatial matched filter beam-pattern is characterized analytically here in this paper. This paper shows that the beamformer would suffer a high pointing bias if the figure-8 sensors' directivity-order exceeds one. Other characteristics of the beam-pattern are also analytically explained herein. [ABSTRACT FROM AUTHOR]

The multi-path and dispersion properties of shallow water waveguides make conventional beamforming (CBF) face issues such as beam shift, broadening, splitting, output distortion, and array gain reduction. In this paper, the striation-based beamforming (SBF) is investigated to address these issues. SBF differs from CBF by utilizing frequency-shift processing along interference striations. The performance difference between CBF and SBF is compared. It demonstrates that under ideal waveguide modeling with pulse sources, SBF can achieve a beam output response that is close to the plane wave condition. The speed term of SBF's response is approximately independent of modal indexes, which equips SBF to form a unique beam output and guarantee the beam resolution. The processing of consistent signals along the striation maintains the optimal signal correlation, which makes SBF ensure the output fidelity and array gain. To shift the mainlobe of SBF to the source azimuth, the time delay related to the waveform truncation point can be introduced to pre-compensate the array signals. There exist two theoretical accuracy limits to using the truncation. First, truncation time corresponds to the waveform point at r 0 / c (r0 is the source range), and the mainlobe of SBF is directed to the source azimuth. Second, truncation corresponds to the pulse peak point, and the azimuth estimation accuracy of SBF gets close to CBF. Simulations and experimental results are given as illustrations. [ABSTRACT FROM AUTHOR]

This paper presents an underwater soundfield visualisation method for passive-sonar applications employing circular hydrophone arrays. The method operates by segregating the space by means of beamforming into angular sectors scanning the whole horizontal plane and then computing acoustic parameters within each sector. The information from these directionally constrained parameters is fused in order to produce spatial spectra which depict the distribution of acoustic energy over bearing. The evaluation is performed on simulated data of circular hydrophone arrays mounted on rigid cylindrical baffles. Comparisons against baseline methods of similar computational complexity suggest that, for moderate to high signal-to-noise ratio levels, the proposed method offers improved performance in terms of background noise suppression, angular resolution, and direction-of-arrival estimation accuracy. Additionally, it is demonstrated that, with the appropriate choice of sector pattern, the proposed method can, at least in some cases, achieve superior performance to the baseline methods in the presence of interferers even at low signal-to-interference ratio levels. Last, the sector-based parameter diffuseness, which is directly related to the direct-to-diffuse ratio, may be used both as a weight function to further attenuate the background noise level and as a confidence measure of the estimation accuracy. [ABSTRACT FROM AUTHOR]

A publication by McCargar and Zurk [J. Acoust. Soc. Am. 133(4), EL320–EL325 (2013)] introduced a passive source depth estimation method for a moving tonal source with a vertical line array (VLA), utilizing the depth-dependent modulation in the arrival angle domain caused by the interference between the direct and surface-reflected acoustic arrivals. Under the isovelocity approximation, this method can estimate the depth of sources at close ranges, but the depth estimation error will increase with the increase in source range, as the impact of the sound speed profile on sound propagation is ignored. This paper presents a theoretical formula for calculating the modeled interference structure in the arrival angle domain with the knowledge of the sound speed profile. By matching the measured interference structure obtained from the beamforming of the acoustic data received by the VLA with the modeled structure under different assumed source depths, the tonal source depth estimation is achieved, even for sources at the remote part of the direct arrival zone. The performance of this method is verified by simulation data, as well as experimental data radiated from a towed source and a non-cooperative passing ship. [ABSTRACT FROM AUTHOR]

This paper presents gridless sparse processing for direction-of-arrival (DOA) estimation. The method solves a gridless version of sparse covariance-based estimation using alternating projections. Gridless sparse DOA estimation is represented by the reconstruction of Toeplitz-structured low-rank matrices, which our method recovers by alternatively projecting a solution matrix. Compared to the existing gridless sparse methods, our method improves speed and accuracy and considers non-uniformly configured linear arrays. High-resolution and reliable DOA estimation are achieved even with single-snapshot data, coherent sources, and non-uniform arrays. Simulation results demonstrate performance improvements compared to the existing DOA estimators, including gridless sparse methods. The method is illustrated using experimental data from a real ocean experiment. [ABSTRACT FROM AUTHOR]

Ray-based blind deconvolution (RBD) is a method that estimates the source waveform and channel impulse response (CIR) using the ray arrival in an underwater environment. The RBD estimates the phase of the source waveform by using beamforming. However, low sampling, array shape deformation, and other factors can cause phase errors in the beamforming results. In this paper, phase correction is applied to the beamforming estimated source phase to improve RBD performance. The impulsiveness of the CIR was used as additional information to correct the initially estimated source phase. Kurtosis was used to measure impulsiveness, and the phase correction that maximized the kurtosis of the CIRs was calculated through optimization. The proposed approach is called ray-based blind deconvolution with maximum kurtosis phase correction (RBD-MKPC) and is based on a single-input multiple-output system. The RBD-MKPC was tested with several CIRs and source waveform combinations in the shallow-water acoustic variability experiment 2015 using broadband high-frequency pulses (11–31 kHz) as the source and a sparse vertical 16-element line array as receivers. The results indicate that the RBD-MKPC improves the estimation performance. In addition, from an optimization point of view and compared with other initialization methods, the proposed method showed superior convergence speed and estimation performance. [ABSTRACT FROM AUTHOR]

The bubble is an effective carrier of underwater gas leakage detection, and its rapid localization is of great significance. In this paper, a continuous underwater bubble localization method using passive acoustic array beamforming is proposed. A specific array of hydrophones was constructed to capture the bubble acoustic signal. The signal of finite length is obtained by introducing the time window. Then, the time delay compensation is carried out according to the positions of the hydrophones in the array to estimate the direction of arrival. The influence of shape, number, and aperture of the hydrophone array on the algorithm performance is studied by simulation. Moreover, the experiment was carried out in a cuboid transparent tempered glass water tank in the laboratory, and a field programmable gate array is used as the core control unit of the data processing system to complete the signal acquisition and the algorithm execution. The localization results show that the passive acoustic cross array beamforming algorithm can accurately and stably estimate the leakage direction at different positions. [ABSTRACT FROM AUTHOR]

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]

In this paper, the case considered is a planar microphone array placed in front of a wall of the room so that the microphone array plane is perpendicular to that of the wall. For this arrangement, a so-called half-space propagation model has been recently proposed, which accounts for the joint contribution of the direct path and the earliest reflection introduced by the adjacent wall. Based on this propagation model, a numerical process to estimate a model of the diffuse noise spatial coherence, which accounts for the presence of the reflecting surface, is proposed. The suggested noise covariance model is used in order to extend the superdirective beamformer in half-space, achieving notable improvements in performance in comparison to a more typical implementation that involves the spherical isotropic coherence model. [ABSTRACT FROM AUTHOR]

Sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In contrast, uniform linear arrays (ULA) cannot resolve more sources than the number of sensors. This paper demonstrates this using Sparse Bayesian learning (SBL) and co-array MUSIC for single frequency beamforming. For approximately the same number of sensors, co-prime and nested arrays are shown to outperform ULA in root mean squared error. This paper shows that multi-frequency SBL can significantly reduce spatial aliasing. The effects of different sparse sub-arrays on SBL performance are compared qualitatively using the Noise Correlation 2009 experimental data set. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of one-eighth Spherical Fraction Microphone Array through experimental measurement to analyze acoustic scenes in one-eighth of space. The array geometry is designed to be placed in a room corner at the junction of three acoustically rigid walls. Two prototypes are built with 8 and 16 microphones, respectively. The sampling strategy is discussed and a spatial aliasing analysis is carried out both analytically and by numerical simulations. The array performances are evaluated through Spherical Fraction Beamforming (SFB). This approach is based on the decomposition of the acoustic pressure field in a rigid bounded domain. The localization angular error and Directivity Index criterion are evaluated for both arrays. In a first experiment, the arrays are mounted in an eighth of space built inside an anechoic room. The results are compared with simulation and show consistency. The theoretical limitations of SFB in a rigid bounded one-eighth of space are retrieved experimentally. These limitations are also observed in a real configuration: an office room. Further investigations on SFB are also conducted in the case of a virtual scene constructed with two sound sources. [ABSTRACT FROM AUTHOR]

Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high-dimensional data that are broken down to spectra by integrating spatial regions of interest. This paper presents two methods that enable the automated identification of aeroacoustic sources in sparse beamforming maps and the extraction of their corresponding spectra to overcome the manual definition of regions of interest. The methods are evaluated on two scaled airframe half-model wind tunnel measurements and on a generic monopole source. The first relies on the spatial normal distribution of aeroacoustic broadband sources in sparse beamforming maps. The second uses hierarchical clustering methods. Both methods are robust to statistical noise and predict the existence, location, and spatial probability estimation for sources based on which regions of interest are automatically determined. [ABSTRACT FROM AUTHOR]

A coprime sensor array (CSA) is a sparse array geometry that interleaves two spatially undersampled uniform linear arrays (ULAs) with coprime undersampling factors. The CSA Min processor achieves an asymptotically unbiased spatial power spectral density (PSD) estimate while approaching the variance of a ULA conventional beamformer. Nonstationary underwater sonar environments often preclude the number of snapshots required to achieve a desirable PSD variance. The multitaper method improves PSD variance by O(K) at the expense of resolution without additional snapshot cost by averaging uncorrelated PSD estimates obtained using a set of K orthogonal tapers. This paper proposes the multitapered Min processor to achieve unambiguous PSD estimates with desirable variance properties for passive beamforming scenarios. The probability density function and the first two moments of the MT-Min processor's PSD estimate are derived in closed-form for spatially white Gaussian processes. Simulations verify the variance reduction predicted by the analytical derivation for white processes and, by extension, for non-white processes. The multitaper method is then extended to an ad hoc mixture of Min and Product processors under constant noise plateau normalization that attenuates the spurious peaks occurring in the CSA PSD estimates in the presence of multiple planewave arrivals. [ABSTRACT FROM AUTHOR]

This paper proposes efficient algorithms for accurate recovery of direction-of-arrivals (DoAs) of sources from single-snapshot measurements using compressed beamforming (CBF). In CBF, the conventional sensor array signal model is cast as an underdetermined complex-valued linear regression model and sparse signal recovery methods are used for solving the DoA finding problem. A complex-valued pathwise weighted elastic net (c-PW-WEN) algorithm is developed that finds solutions at the knots of penalty parameter values over a path (or grid) of elastic net (EN) tuning parameter values. c-PW-WEN also computes least absolute shrinkage and selection operator (LASSO) or weighted LASSO in its path. A sequential adaptive EN (SAEN) method is then proposed that is based on c-PW-WEN algorithm with adaptive weights that depend on previous solution. Extensive simulation studies illustrate that SAEN improves the probability of exact recovery of true support compared to conventional sparse signal recovery approaches such as LASSO, EN, or orthogonal matching pursuit in several challenging multiple target scenarios. The effectiveness of SAEN is more pronounced in the presence of high mutual coherence. [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]

Techniques have been studied for the localization of an underground source with seismic interrogation signals. Much of the work has involved defining either a P-wave acoustic model or a dispersive surface wave model to the received signal and applying the time-delay processing technique and frequency-wavenumber processing to determine the location of the underground tunnel. Considering the case of determining the location of an underground tunnel, this paper proposed two physical models, the acoustic approximation ray tracing model and the finite difference time domain three-dimensional (3D) elastic wave model to represent the received seismic signal. Two localization algorithms, beamforming and Bayesian inversion, are developed for each physical model. The beam-forming algorithms implemented are the modified time-and-delay beamformer and the F-K beamformer. Inversion is posed as an optimization problem to estimate the unknown position variable using the described physical forward models. The proposed four methodologies are demonstrated and compared using seismic signals recorded by geophones set up on ground surface generated by a surface seismic excitation. The examples show that for field data, inversion for localization is most advantageous when the forward model completely describe all the elastic wave components as is the case of the FDTD 3D elastic model. [ABSTRACT FROM AUTHOR]

This paper studies signal models for microphone array beamforming in the short-time-Fourier-transform (STFT) domain with long acoustic impulse responses. The major contributions are as follows. First, the signal modeling problem is investigated in the STFT domain and a general decomposition is proposed for the convolved source signal. Second, new insights into the array manifold are presented: the STFT of the windowed acoustic impulse response from the source to the sensors. Third, the structure of the reference signal is analyzed: it can be viewed as the output of a beamformer without considering the noise in the observation signal. Fourth, based on the new perspectives and decomposition, a signal model is derived based on the use of the superdirective beamformer. Finally, three performance measures are defined, based on which three optimal/suboptimal signal models are derived and their performance is assessed under different acoustic environments and analysis window lengths. The performance of the well-known minimum variance distortionless response (MVDR) beamformer is evaluated, which justifies the properties of the developed signal models. [ABSTRACT FROM AUTHOR]

Matched filter (MF) and conventional beamforming (CBF) are widely used in active sonar; the performance of the former (temporal resolution) is limited by the signal bandwidth, and that of the latter (angular resolution) is limited by the array aperture. Previous work has shown that angular resolution can be significantly improved by deconvolving the CBF outputs. In this paper, deconvolution is extended to the time domain by deconvolving the MF outputs, and a high-resolution two-dimensional deconvolution method is proposed to simultaneously improve the temporal and angular resolution. Numerical simulations and experimental tank data show that angular resolutions are improved 26 times, and temporal resolutions are improved 10 times compared with the conventional MF and CBF methods. Reverberations are much suppressed to allow target echoes to be detected from the received time series data. [ABSTRACT FROM AUTHOR]

Localization of acoustic sources using a sensor array is typically performed by estimating direction-of-arrival (DOA) via beamforming of the signals recorded by all elements. Software-based conventional beamforming (CBF) forces a trade-off between memory usage and direction resolution, since time delays associated with a set of directions over which the beamformer is steered must be pre-computed and stored, limiting the number of look directions to available platform memory. This paper describes a DOA localization method that is memory-efficient for three-dimensional (3D) beamforming applications. Its key lies in reducing 3D look directions [described by azimuth/inclination angles (ϕ , θ) when considering the array as a whole] to a single variable (a conical angle, ζ) by treating the array as a collection of sensor pairs. This insight reduces the set of look directions from two dimensions to one, enabling computational and memory efficiency improvements and thus allowing direction resolution to be increased. This method is described and compared to CBF, with comparisons provided for accuracy, computational speedup, and memory usage. As this method involves the incoherent summation of sensor pair outputs, gain is limited, restricting its use to localization of strong sources—e.g., for real-time acoustic localization on embedded systems, where computation and/or memory are limited. [ABSTRACT FROM AUTHOR]

The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment. [ABSTRACT FROM AUTHOR]

The array invariant proposed for robust source localization in shallow water exploits the beam-time migration of broadband signals. The approach requires minimal knowledge of the environment, but involves plane wave beamforming using a short-aperture vertical array in stratified acoustic waveguides. In this paper, the array invariant approach is extended to a large-aperture vertical array that is sparse with significant variation of the sound speed across the aperture for conventional beaforming. The extension is feasible because the array invariant in shallow water utilizes surface/bottom- reflected arrivals that behave like those in ideal waveguides. Robust source-range estimation is demonstrated using data from a 16-element, 56-m long vertical array at various ranges (1.5–3.5 km) from a broadband source (0.5–2 kHz) in approximately 100-m deep shallow water. [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]

In the field of noise identification with microphone arrays, conventional delay-and-sum (DAS) beamforming is the most popular signal processing technique. However, acoustic imaging results that are generated by DAS beamforming are easily influenced by background noise, particularly for in situ wind tunnel tests. Even when arithmetic averaging is used to statistically remove the interference from the background noise, the results are far from perfect because the interference from the coherent background noise is still present. In addition, DAS beamforming based on arithmetic averaging fails to deliver real-time computational capability. An observer-based approach is introduced in this paper. This so-called observer-based beamforming method has a recursive form similar to the state observer in classical control theory, thus holds a real-time computational capability. In addition, coherent background noise can be gradually rejected in iterations. Theoretical derivations of the observer-based beamforming algorithm are carefully developed in this paper. Two numerical simulations demonstrate the good coherent background noise rejection and real-time computational capability of the observer-based beamforming, which therefore can be regarded as an attractive algorithm for acoustic array signal processing. [ABSTRACT FROM AUTHOR]

Conventional synthetic aperture processing uses motion of the sonar to increase aperture size and bearing resolution. Two recent papers discussed a different application in which synthetic elements are used to fill in an otherwise sparse passive array. This paper points out that ambiguities persist, even with synthetic elements, in the ideal case of a straight, uniform, sparse line array with constant velocity in the presence of plane wave signals. It is also shown that irregular motion such as acceleration introduces additional information which can be exploited to suppress the ambiguities. The degree of suppression in such an approach is independent of signal direction. If source stability supports extended coherent integration, then the acceleration and integration time required are both modest to achieve interesting levels of suppression. For a less stable source, a modified conventional beamformer is introduced which leverages acceleration over multiple snapshots to suppress the ambiguities. A post-beamformed processing stage involving a nonlinear deconvolution technique such as the CLEAN algorithm can further improve the result. A semi-coherent adaptation of CLEAN is shown to remove the residual ambiguities effectively in the presence of a moderate level of uncorrelated noise. [ABSTRACT FROM AUTHOR]

The paper describes the beamforming procedures in an acoustic waveguide based on representing the field on the antenna as a superposition of several stable components formed by narrow beams of rays [A. L. Virovlyansky, J. Acoust. Soc. Am. 141, 1180–1189 (2017)]. A modification of the matched field processing method is proposed, based on the transition from comparing the measured and calculated fields on the antenna to comparing their stable components. The modified approach becomes less sensitive to the inevitable inaccuracies of the environmental model. In the case of a pulsed source, the stable components carry signals whose arrival times can be taken as input parameters in solving the inverse problems. The use of the stable components as the initial fields on the aperture of the emitting antenna makes it possible to excite narrow continuous wave beams propagating along given ray paths. [ABSTRACT FROM AUTHOR]

Differential beamforming combined with microphone arrays can be used in a wide range of applications related to acoustic and speech signal acquisition and recovery. A practical and useful method for designing differential beamformers is the so-called null-constrained method, which was developed based on linear arrays and requires only the nulls' information from the target directivity pattern. While it is effective and easy to use, this method is found not suitable for designing steerable differential beamformers with circular arrays. This paper reexamines this technique in the context of circular differential microphone arrays. By analyzing the properties of the circular array topology, the null-constrained method is extended to include symmetric constraints, which is inherent in the design of circular arrays. This extension yields a design method for fully steerable differential beamformers that require only minimum information from the target beampattern. Simulations justify the theoretical analysis and demonstrate the good properties of the developed method. [ABSTRACT FROM AUTHOR]

Identifications of rotating sound sources are of interest in many industrial applications. Nowadays, almost all frequency-domain rotating beamformings are based on the concept of virtual rotating array (VRA), and consequently, have one constraint that microphones must constitute a ring around the rotation axis of a sound source. This constraint greatly restricts the wide application of these beamformings in industrial experiments. To overcome this constraint, this paper proposed a frequency-domain rotating beamforming (mode composition beamforming, MCB). Without using the concept of VRA, MCB directly uses the concept of delay-and-sum with the mode composition of rotating sound. MCB has the same ability to rotate sound source identification as the famous time-domain rotating source identifier (ROSI) proposed by Sijtsma et al. (2001) and generally has a much higher computational speed than ROSI, especially when a large number of samplers are used. Special arrays, such as rotational symmetry array, with respect to the rotation axis with a high order of rotational symmetry, are recommended to compensate for the ghost contributions in MCB induced by the Doppler effect. [ABSTRACT FROM AUTHOR]

Conventional compressive beamforming assumes that the acoustic sources fall on the discretized grid points. The performance degrades when the acoustic source lies off the discretized grid point, that is, when the basis mismatch occurs. This paper proposes a two-dimensional Newtonized orthogonal matching pursuit compressive beamforming, including single and multiple snapshot versions, which constructs the maximum likelihood estimation model, taking the position and strength of sources on a two-dimensional continuous plane as parameters. This method first captures the grid point near the source based on the discrete grid. Then it optimizes the coordinate estimation within the local continuous plane by a combination of the two-dimensional Newton optimization and a feedback mechanism to converge to the actual source position. It allows acoustic source identification in the near field utilizing arbitrary geometry planar array and works without the prior knowledge of signal-to-noise ratio and/or regularization parameters. Simulations and experiments show that the proposed method can overcome the basis mismatch issue and provide high spatial resolution, obtaining an accurate estimation for the position and strength of the acoustic source. Moreover, the multiple snapshot version outperforms the single snapshot version, especially under low signal-to-noise ratio. The larger the number of snapshots, the better the performance. [ABSTRACT FROM AUTHOR]

A conventional structural Luneburg lens is a symmetric circular gradient-index lens with refractive indices decreasing from the centre along the radial direction. In this paper, a flattened structural Luneburg lens (FSLL) based on structural thickness variations is designed by using the quasi-conformal transformation technique. Through numerical simulations and experimental studies, the FSLL is demonstrated to have excellent beam steering performance for the manipulation of flexural wave propagation at desired angles. [ABSTRACT FROM AUTHOR]

Identifications of rotating sound sources are of interest in many industrial applications. The most famous beamforming to identify rotating sound source is the time-domain ROSI beamforming proposed by Sijtsma et al. (AIAA 2001-2167, 2001). Nowadays ROSI is still the unique widely recognized beamforming for array with arbitrary microphone configuration to identify rotating sound source. De-Dopplerisation is essential for beamforming to identify a moving sound source. This paper is devoted to the identification and elimination of the Doppler effect in ROSI. A non-zero source with a fundamental frequency may induce ghost contributions in ROSI at other scan points different from this source position, at all side band frequencies to the fundamental frequency with a modulation frequency equal to the rotation frequency. A rotational symmetry array with respect to rotation axis of sound source can be used to eliminate the ghost contributions in ROSI at side bands that are not non-zero integer multiples on the order of rotational symmetry with respect to the rotation axis of sound source of the array. [ABSTRACT FROM AUTHOR]

For a sound field observed on a planar sensor array, compressive sensing (CS) reconstructs the two-dimensional (2D) direction-of-arrival (DOA) of multiple sources using a sparsity constraint. Conventional compressive beamforming methods suffer from grid mismatch, where true DOAs do not fall on the discretized angular search grid. This paper adopts a CS-based model, which can reconstruct block-sparse signals, and the model treats DOAs and the off-grid DOA compensation parts as blocks to deal with the off-grid 2D beamforming. The method is illustrated by numerical simulations and shows high estimation accuracy. Also, the approach does not require a specific array configuration and is suitable for arbitrary planar array geometry, which is practically useful. Since propeller tip vortex cavitation induces noise sources located sparsely near the propeller tip, the high-resolution of the method is demonstrated with experimental data from cavitation tunnel experiments. [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 a measurement technique aimed at reducing or possibly eliminating the spatial aliasing problem in the beamforming technique. Beamforming main disadvantages are a poor spatial resolution, at low frequency, and the spatial aliasing problem, at higher frequency, leading to the identification of false sources. The idea is to move the microphone array during the measurement operation. In this paper, the proposed approach is theoretically and numerically investigated by means of simple sound propagation models, proving its efficiency in reducing the spatial aliasing. A number of different array configurations are numerically investigated together with the most important parameters governing this measurement technique. A set of numerical results concerning the case of a planar rotating array is shown, together with a first experimental validation of the method. [ABSTRACT FROM AUTHOR]

Patch near-field acoustical holography methods like statistically optimized near-field acoustical holography and equivalent source method are limited to relatively low frequencies, where the average array-element spacing is less than half of the acoustic wavelength, while beamforming provides useful resolution only at medium-to-high frequencies. With adequate array design, both methods can be used with the same array. But for holography to provide good low-frequency resolution, a small measurement distance is needed, whereas beamforming requires a larger distance to limit sidelobe issues. The wideband holography method of the present paper was developed to overcome that practical conflict. Only a single measurement is needed at a relatively short distance and a single result is obtained covering the full frequency range. The method uses the principles of compressed sensing: A sparse sound field representation is assumed with a chosen set of basis functions, a measurement is taken with an irregular array, and the inverse problem is solved with a method that enforces sparsity in the coefficient vector. Instead of using regularization based on the 1-norm of the coefficient vector, an iterative solution procedure is used that promotes sparsity. The iterative method is shown to provide very similar results in most cases and to be computationally much more efficient. [ABSTRACT FROM AUTHOR]