Conventional Beamforming Algorithm Research Articles

Aeroacoustic source localization using the microphone array method with application to wind turbine noise

The deconvolution DAMAS algorithm can effectively eliminate the misconceptions in the usually-used beamforming localization algorithm, allowing for a more accurate calculation of the source location as well as the intensity. When solving a linear system of equations, the DAMAS algorithm takes into account the mutual influence of different locations, reducing or even eliminating sidelobes and producing more accurate results. This work first introduces the principles of the DAMAS algorithm. Then it applies both the conventional beamforming algorithm and the DAMAS algorithm to simulate the localization of a single-frequency source from a 1.5 MW wind turbine, a complex line source with the text “UCAS” and a line source downstream of an airfoil trailing edge. Finally, the work presents experimental localization results of the source of a 1.5 MW wind turbine using both the conventional beamforming algorithm and the DAMAS algorithm.

Comparison of conventional and adaptive acoustic beamforming algorithms using a tetrahedral microphone array in noisy environments

In situ acoustic measurements are often plagued by interfering sound sources that occur within the measurement environment. Both adaptive and conventional beamforming algorithms, when applied to the outputs of a microphone array arranged in a tetrahedral geometry, are able to capture sound sources in desired directions and reject sound from unwanted directions. Adaptive algorithms may be able to measure a desired sound source with greater spatial precision, but require more calculations and, therefore, computational power. A conventional frequency-domain phase-shift algorithm and a modified adaptive frequency-domain Minimum Variance Distortionless Response (MVDR) algorithm were applied to simulated and recorded signals from a tetrahedral array of omnidirectional microphones. The algorithms are described mathematically and demonstrated on both deterministic and real-world sound data, to quantitatively validate and compare their performance and to provide listening examples of their outputs in a variety of acoustically replicated environments. [Work supported by Portland State University.]

Virtual array element beamforming algorithm based on LSTM neural network

Virtual array element beamforming technology can improve the angular resolution of conventional beamforming algorithm. However, when the number of real array elements is limited, the beam pattern of the virtual array element beamforming algorithm based on linear prediction(the VAEBF-LP) is slightly distorted, the root mean square error(RMSE) of Direction of Arrival(DOA) is larger and so is the main lobe width. In response to this problem, A virtual array element beamforming algorithm based on the LSTM neural network(the VAEBF-LSTM) is proposed. This algorithm uses the LSTM neural network to learn the temporal characteristics between the received data of real array elements and the virtual array elements. Obtaining the received data of virtual array elements, this algorithm enables the beam pattern to be free of distortion and achieve higher angular resolution detection of targets with a relatively low RMSE of DOA in this context. The simulation results show that the VAEBF-LSTM not only makes the beam pattern distortion disappear but also makes the main lobe smaller in width than that of the conventional beamforming algorithm with the guarantee that the RMSE of DOA remains considerably low when the number of real array elements is limited.

Conventional Beamforming Algorithm Based on Polarization Filtering for an Acoustic Vector-Sensor Linear Array Mounted Near a Baffle

Conventional Beamforming Algorithm Based on Polarization Filtering for an Acoustic Vector-Sensor Linear Array Mounted Near a Baffle

Beam-domain deconvolution beamforming algorithm based on compressive sensing

Deconvolution beamforming is often used to improve the azimuth resolution in target detection. However, most traditional deconvolution beamforming algorithms require the array directivity function to be shift-invariant, which are suitable for specific arrays such as linear arrays and circular arrays. A beam-domain deconvolution beamforming method suitable for arbitrary array is proposed based on compressive sensing. First, conventional beamforming is used to obtain several complex beam outputs, then the Sparse Bayesian Learning (SBL) reconstruction algorithm is applied to the beam-domain model to achieve deconvolution of complex beam outputs. The deconvolution process leads to more accurate estimation for the Direction Of Arrival (DOA) for target signals. The proposed method can also effectively reduce the computational complexity of the algorithm by controlling the number of output beams from the conventional beamforming and is applicable for both non coherent and coherent signals. The simulation and experiment results show that the proposed algorithm has azimuth resolution performance comparable to the traditional element-domain SBL beamforming algorithm, and is superior to the conventional beamforming and Minimum Variance Distortionless Response (MVDR) algorithms. When applied to short and dense arrays, the computation complexity of the proposed algorithm is significantly lower than that of the traditional element-domain SBL beamforming algorithm.

Design and characterisation of a 3-D microphone array for windtunnel measurements

A three-dimensional (3-D) array of GRAS 40PH-10 CCP Free-field Array Microphones has been designed for use in the UNSW anechoic wind tunnel (UAT) for aeroacoustic measurements. The array consists of 192 microphones split into three identical planar arrays of 64 microphones which are placed on both sides and above the potential core of the open-jet that issues into the wind tunnel test section. The planar arrays feature an Underbrink spiral comprised of eight arms of eight microphones each. The spiral configuration was selected to maximize signal-to-noise ratio (SNR) and minimize the beamwidth given the geometrical restrictions of the UAT. A white noise speaker was placed at a range of locations within the testing domain to characterize the source localization abilities of the array as a function of position and frequency. Furthermore, a pair of independent white noise speakers were placed at various separation distances to determine the array’s capability to identify multiple sources within the domain. A 3-D implementation of conventional cross-spectral beamforming and Clean-SC deconvolution algorithms was used, and the performance of each was compared. It was determined that Clean-SC was the preferred method for 3-D beamforming in terms of source localization accuracy, SNR, and beamwidth, despite the higher computational cost.

Direction-of-arrival estimation for acoustic signals based on direction-dependent parameter tuning of a bioinspired binaural coupling system

Bioinspired methods for sound source localization offer opportunities for resource reduction as well as concurrent performance improvement in contrast to conventional techniques. Usually, sound source localization requires a large number of microphones arranged in irregular geometries, and thus has high resource requirements in terms of space and data processing. Motivated by biology and using digital signal processing methods, an approach that adapts the coupled hearing system of the fly Ormia ochracea with a minimally distant two-microphone array is presented. Despite its physiology, the fly is able to overcome physical limitations in localizing low-frequency sound sources. By exploiting the filtering effect of the coupling system, the direction-of-arrival of the sound is determined with two microphones at an intermediate distance of 0.06 m. For conventional beamforming algorithms, these physical limitations would result in degraded localization performance. In this work, the bioinspired coupling system is analyzed and subsequently parameterized direction-sensitive for different directions of incidence of the sound. For the parameterization, an optimization method is presented which can be adopted for excitation with plane as well as spherical sound wave propagation. Finally, the method was assessed using simulated and measured data. For 90% of the simulated scenarios, the correct direction of incidence could be determined with an accuracy of less than 1∘ despite the use of a minimal distant two-microphone array. The experiments with measured data also resulted in a correct determination of the direction of incidence, which qualifies the bioinspired method for practical use in digital hardware systems.

Ultrasonic adaptive plane wave high-resolution imaging based on convolutional neural network

Components with complex surfaces can pose a challenge for achieving phased array ultrasonic testing. Although coupling issues can be addressed by immersing or adding plexiglass wedges, obtaining high-quality images in a short time remains difficult under such conditions. This paper proposes an adaptive plane wave imaging scheme to address these challenges. First, the interface is reconstructed using a plane wave priority echo estimation method. Then, a beamforming algorithm adapted to the irregular interface is used to generate low-resolution (LR) images, which are then reconstructed into high-resolution (HR) images in a short time with convolutional neural network (CNN). The CNN is trained with simulation data, and the defects of test blocks and wheel rim are tested. Experimental results show that the reconstruction method combined with CNN significantly improves imaging quality and speed compared to the conventional adaptive plane wave beamforming algorithm based on the time-domain physical model.

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array.

Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge.

Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

Analog beamforming (ABF) architectures for both large-scale antennas at the base station (BS) and the small-scale antennas at the user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of the ABF network that accepts input as the channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple-input multiple-output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neural network (DNN) enabled beamforming (BF) design of mmWave M-MIMO system compared with the conventional BF algorithms, while lessening the computational complexity.

Direction of arrival and target scale estimation based on split beamforming algorithm

Based on the theory of split beamforming algorithm and the experimental data collected by circular array, this paper analyzes the characteristics of DOA and target size estimation. In this paper, the signal receiving model of planar circular array and the split beamforming algorithm are studied. Then, based on the data collected from the lake experiment, the DOA estimation results of conventional beamforming algorithm, multiple signal classification algorithm and split beamforming algorithm are analyzed, and the target scale estimation results of split beamforming algorithm is analyzed. The experimental data analysis shows that the split beamforming algorithm has good direction finding stability, and the target scale estimation error based on the split beamforming algorithm is small.

Adaptive broadband frequency invariant beamforming using nulling-broadening and frequency constraints

The performance of conventional adaptive broadband beamforming algorithms is degraded because of rapidly moving interferences and look-direction errors. In this paper, a number of techniques are proposed to overcome this issue. By proposing an adaptive frequency invariant beamformer (FIB) that uses frequency constraints, we can obtain undistorted responses to broadband signal of interests (SOIs) and constant beamwidths over an entire frequency band without pre-steering delays. And the influence of look-direction errors can also be receded. Furthermore, a singular value decomposition of the frequency constraints is added to the proposed algorithm in view of releasing numerous degrees of freedom (DOFs), as this approach can simultaneously maintain the enhanced anti-interference performance and hold the undistorted response to SOIs. Finally, the nulling-broadening method involving the use of virtual interferences is adopted to construct a taper matrix and ensure robust performance versus rapidly moving interferences. This robust response to rapidly moving interferences and look-direction errors allows the proposed algorithm to achieve satisfactory broadband beamforming performance. Simulation results highlight the correctness and effectiveness of the proposed methods.

Pruned Distributed and Parallel Subarray Beamforming for 3-D Underwater Imaging With Fine-Grid Sparse Arrays

The development of real-time 3-D underwater imaging systems with large planar arrays involves high hardware costs and great computational burden. This study proposes the concept of fine-grid sparse arrays and the pruned distributed and parallel subarray (P-DPS) beamforming algorithm to achieve 3-D narrowband underwater imaging systems with low complexity. Fine-grid sparse arrays refine traditional half-wavelength grids to improve the freedom of the sparse optimization process and reduce the number of active elements. However, grid refinement increases the computational load of conventional fast beamforming algorithms. To solve this problem, a new pruned fast Fourier transform is proposed to eliminate all the redundant operations in the P-DPS beamforming, which is suitable for fine-grid sparse arrays. The computational load of the P-DPS beamforming seldom increases with grid refinement. The validity of the proposed method is verified with a fine-grid sparse planar array designed in the study. The computational load is then analyzed and compared with those of other methods. Results show the notable improvements in array sparsity rate and computational efficiency relative to those in the literature.

Salp swarm optimization in hybrid beamforming for MIMO radar

The Multi Input Multi Output (MIMO) radar waveform diversity Significantly improves parameter identifiably than phased-array radar performance. Precoding, combining and spatial multiplexing techniques improves the data throughput and reliability of the transmission in MIMO systems. But increment in transmit and receive elements in MIMO antenna array induces considerable increase in required power for hardware and computation cost. Hybrid beamforming employs fewer RF-to-baseband chains. With conscious selection of the weights for pre-coding and combining, hybrid beamforming establishes perfect trade-off between complexity, performance, cost, and power consumption in practical applications. Performance of MIMO radar system can be improved using newly developed bio inspired metaheuristic algorithms as compared to conventional and adaptive beamforming algorithms. In this work the Salp Swarm algorithm (SSA) is implemented to optimize the performance of hybrid beamforming using Raleigh channel and considering the bit error rate and normalized array power parameters. The swarming behavior of salps when navigating and foraging in oceans is the inspiration behind the SSA optimization algorithm. The obtained results are compared with the conventional phase-shift as well as adaptive linearly constrained minimum variance beamforming algorithms on simulation platform with standard considerations. It is observed that this new approach of Salp swarm algorithm is having improved and much better performance with the considered parameters.

A DOA estimation algorithm based on greedy recovery under the condition of array element failure

Aiming at the DOA estimation when some elements in the hydrophone array fail, the conventional beamforming algorithm is not effective in estimating the direction of arrival. This paper proposes a beamforming algorithm based on greedy recovery. The ideal conditions and After the estimation effect under the failure condition of the array element, the detection performance of the method is compared and analyzed by simulation. The results show that when the failure ratio of the array element reaches 70% or more, the method always keeps the effective identification of the direction of arrival, and has better detection performance and tolerance than traditional algorithms.

Evaluation of the noise impact of flap-tip fences installed on laminar wings

The aeroacoustics of laminar wings and the noise abatement provided by flap-tip fences are studied experimentally on a scaled model installed in the closed test section of a subsonic wind tunnel. The activity has been developed under an international project funded by the European Union through the H2020 Framework Program. The model reproduces an innovative regional aircraft mounting aft-engines and adopting the Natural Laminar Flow concept. Both take-off and landing settings are tested for several combinations of wind tunnel speeds and angles of attack; these configurations being the most critical from the viewpoint of airframe noise generation. A baseline configuration without flap-tip fences is also tested for a comparative study. Noise sources are identified by measuring pressure fluctuations through a phased microphone array. Data are processed using both Conventional Beamforming and CLEAN-SC algorithms to retrieve the sound source maps and the integrated spectra over the areas of interest. Directivity effects are investigated as well by moving the microphone array in different axial positions corresponding to different aircraft polar directivity angles. The wind tunnel data are eventually extrapolated to full scale and projected to flight condition allowing the analysis of the results in terms of Effective Perceived Noise Level (EPNL). Tests provided an extensive characterization of the acoustic behavior of the analyzed model demonstrating the capability of measurements carried out in a non-anechoic environment, to provide reliable data. The longitudinal traversing of the microphones array allowed us to compute, even though qualitatively, the EPNL and thus demonstrated the feasibility of a procedure that can be a reference for the design of future aeroacoustic tests. The effectiveness of the flap-tip fences to successfully reduce the flap side-edge noise has been definitely verified both through the analysis of the acoustic maps retrieved from the beamforming investigation and by the estimation of the overall variation of the EPNL with respect to a baseline reference configuration without low-noise devices.

Accurate Near-Field Millimeter-Wave Imaging of Concave Objects—A Case Study of Dihedral Structures Under Monostatic Array Configurations

With the fast progress of high-resolution microwave and millimeter-wave (MMW) near-field imaging systems, image artifacts caused by complex high-contrast target become a nonnegligible issue for accurate surface reconstruction and recognition. Among different types of artifacts, those caused by high-order scattering within concave structures are particularly difficult to interpret and to correct. This article focuses on analyzing these unconsidered effects in conventional imaging or beamforming algorithms and chooses a typical imaging scenario with dihedral structures under near-field monostatic cylindrical imaging geometry for discussion. The mechanism of multiple reflections and the formation of artifacts are firstly investigated, and the artifacts are classified into two categories based on the number of reflection times. Then shooting and bouncing rays (SBRs) method is utilized to establish an analytical forward model including the mechanism of multiple reflections. Furthermore, a new imaging algorithm based on this forward model is proposed to obtain more accurate reconstructions. By compensating phase shifts of different multiple reflection signals with the corresponding range equations, dihedral-type objects with different opening angles can be accurately recovered. Numerical results are presented to verify the applicability of the forward model and to validate the effectiveness of the proposed imaging algorithm. In the end, experimental results confirm the practicability of the proposed method.

A joint delay-and-sum and Fourier beamforming method for high frame rate ultrasound imaging

Frame rate is an important metric for ultrasound imaging systems, and high frame rates (HFR) benefit moving-target imaging. One common way to obtain HFR imaging is to transmit a plane wave. Delay-and-sum (DAS) beamformer is a conventional beamforming algorithm, which is simple and has been widely implemented in clinical application. Fourier beamforming is an alternative method for HFR imaging and has high levels of imaging efficiency, imaging speed, and good temporal dynamic characteristics. Nevertheless, the resolution and contrast performance of HFR imaging based on DAS or Fourier beamforming are insufficient due to the single plane wave transmission. To address this problem, a joint DAS and Fourier beamforming method is introduced in this study. The proposed method considers the different distributions of sidelobes in DAS imaging and Fourier imaging and combines the angular spectrum and DAS to reconstruct ultrasound images. The proposed method is evaluated on simulation and experimental phantom datasets to compare its performance with DAS and Fourier beamforming methods. Results demonstrate that the proposed method improves image effective dynamic range and resolution while also retaining a high frame rate of the ultrasound imaging systems. The proposed method improves the effective dynamic range along axial and lateral directions by 10 dB, compared to standard DAS and Fourier beamforming.