Beamforming Method Research Articles

Improved eigenspace-based method for robust adaptive beamforming with dimension search

The eigenspace-based technique, as a classic method for robust adaptive beamforming (RAB), can substantially mitigate the negative impact caused by model mismatch. However, the conventional eigenspace-based beamformer (CESB) undergoes severe performance degradation at low signal-to-noise ratio (SNR). This is because it firmly sets the dimension of the projection subspace (DOPS) to the source number. To alleviate this problem, an improved eigenspace-based approach is proposed in this communication, where the DOPS is auto-selected to adapt to different cases. We roughly analyze the rationality that the robustness of the eigenspace-based technique can be enhanced by simply adjusting the DOPS at low SNR. In addition, we concretely propose an automatic selection method for the DOPS derived from a minimum beamformer sensitivity estimation problem. The proposed method is advantageous in that it is parameter-free. Moreover, simulation experiments are performed to demonstrate the improvement of the CESB and its robustness against steering vector mismatch.

A speech enhancement method combining beamforming with RNN for hearing aids

Speech enhancement is essential for hearing aids. In recent years, many speech enhancement methods based on deep learning have been proven to be effective. However, these speech enhancement methods rarely consider limited hardware resources and have difficulty meeting real-time requirements, which is very important for hearing aids. To solve the above problems, we propose a method that combines beamforming and speech enhancement methods based on deep learning. Beamforming is used to filter background noise and reduce the complexity of noise. Additionally, a new filter bank used in hearing aids is adopted to reduce the complexity of the system. The system was deployed and tested in resource-constrained hearing aids. The effectiveness of the method was verified by objective experiments using standard evaluation indicators. The results showed that the power was 8.43 mA, the signal-to-noise ratio improved by 9.4394 dB, and the PESQ improved by 0.7350. The presented objective and subjective results show that the proposed method achieves better noise suppression than previous methods.

Exploring Background Noise With a Large‐N Infrasound Array: Waterfalls, Thunderstorms, and Earthquakes

AbstractAmbient infrasound noise contains an abundance of information that is typically overlooked due to limitations of typical infrasound arrays. To evaluate the ability of large‐N infrasound arrays to identify weak signals hidden in background noise, we examine data from a 22‐element array in central Idaho, USA, spanning 58 days using a standard beamforming method. Our results include nearly continuous detections of diverse weak signals from infrasonic radiators, sometimes at surprising distances. We observe infrasound from both local (8 km) and distant (195 km) waterfalls. Thunderstorms and earthquakes are also notable sources, with distant thunderstorm infrasound observed from ∼800 to 900 km away. Our findings show that large‐N infrasound arrays can detect very weak signals below instrument and environmental noise floors, including from multiple simultaneous sources, enabling new infrasound monitoring applications and helping map the composition of background noise wavefields.

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

Variational Bayesian Modal Composition Beamforming for fast-rotating axial-fan blade-noise localization and its application condition

The Modal Composition Beamforming (MCB) method can quickly achieve the localization of fast-rotating sound sources; however, its low resolution and unclear applied conditions severely limit its industrial application. This study aims to investigate the MCB-based high-resolution localization method for multiple rotating sound sources and apply it to the localization and identification of axial-fan blade-noise. In this paper, the Variational Bayesian Approximation (VBA) and the Subspace Variational Bayesian Approximation (SVB) methods are used to solve the MCB-based rotating sound power propagation (RSP) model, denoted as RSP-VBA and RSP-SVB, respectively. The effectiveness of the proposed RSP-VBA and RSP-SVB are experimentally validated for the first time. Compared to the conventional MCB method, the resolution is significantly improved, and the proposed high-resolution methods are even faster than the classical Rotating Source Identifier (ROSI) method in most conditions. More importantly, the applied condition of the MCB, RSP-VBA, and RSP-SVB methods is given and verified by using three evaluation indicators. Then a schematic of the applied condition with examples is provided. With the guide of the applied conditions, the RSP-VBA and RSP-SVB methods are applied to the blade-noise localization of various multiblade high-speed axial fans.

Two-way differential strategy for wireless sensor networks

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.

Sound field visualization system by MVDR beamforming with augmented reality and point clouds

In previous research, we proposed an augmented reality (AR) and mixed reality (MR) system visualizing the sound field data measured by the beamforming method. Our system visualized the sound pressure as a colormap on the AR/MR device in real-time. Free viewpoint and visualization of a colormap along the spatial geometry observed by the AR/MR sensor enabled us to understand the location of the sound source. On the other hand, the spacial geometry information of the measurement space was used only for display purposes and not in the processing of sound source location estimation. And then the previous system displaying a flat colormap projected on the spatial geometry has not been able to make measurements that precisely in space. In this paper, we propose a sound field measurement and AR/MR visualization system using point cloud data acquired by light detection and ranging (LiDAR) sensor. The MVDR (minimum variance distortionless response) beamforming calculates the beamforming output by using point cloud data. The sound sources are assumed to be located on point clouds, and the system allows fast localization of a large number of sources. In addition, it is possible to interpret the sound field in various ways based on the scattered sound pressure information. We show some basic experiments conducted in an anechoic chamber.

Expert decision support system to improve the analysis of airframe noise measurements

For the localization and estimation of aeroacoustic sources on complex aircraft models in wind tunnel measurements beamforming and related methods are well known. The task of the aeroacoustic experts is to identify and classify the relevant sources within these beamforming maps. In this paper, we present an expert decision support system to support this task. The system comprises three steps: The first step identifies aeroacoustic sources in sparse beamforming maps and extracts their corresponding spectra without manual definition of regions of interest. The second step calculates the acoustic properties of the sources based on spectral and spatial information. The third step clusters the identified sources based on these acoustic properties. In this paper, data are evaluated based on CLEAN-SC beamforming maps of two airframe half-model wind tunnel measurements. A variety of aeroacoustic features are proposed that capture the characteristics of the source spectra. The result of this expert decision support system is a list of all relevant sources clustered according to their acoustic behavior at different Mach numbers. The aim of the system is to support aeroacoustic specialists in analyzing beamforming results and identifying relevant and spurious sources in-situ.

Direction-of-arrival estimation of impulse noise using MUSIC algorithm

Spatial information about the source of sounds can be obtained by measuring amplitude and phase differences among multiple microphones in a microphone array. Various methods have been proposed for estimating the direction of arrival, including beam-forming methods, but generally their spatial spectral resolution degrades with a low-frequency source in the far-field. In architectural acoustics, it is important to obtain high spatial resolution in the low-frequency band, especially in investigations to understand phenomena such as sudden impulse noise. In this paper, direction-of-arrival estimation using the MUltiple SIgnal Classification (MUSIC) method is discussed. We propose a microphone array structure designed according to the Cramer-Rao lower limit. It is shown that this technique improves the accuracy of elevation angle estimates at low frequencies while maintaining good horizontal angle estimation accuracy. The proposed method was tested in a real building in which impulse noise occurred and its effectiveness in identifying the source direction with high spatial resolution even at low frequencies was confirmed.

AirBP: Monitor Your Blood Pressure with Millimeter-Wave in the Air

Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff based or newly emerging wearable based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this article, we explore contactless BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates the arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size commercial off-the-shelf millimeter-wave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with a mean error of –0.30 mmHg and –0.23 mmHg, as well as a standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.

Grid-free compressive beamforming for arbitrary linear microphone arrays

Grid-free compressive beamforming is a new attractive method for acoustic source identification based on compressive sensing theory, which extracts the source information from the microphone array measurements without discretizing the target source region. In the framework of linear microphone array measurements, many studies have been carried out around this method. They have strict requirements on the distribution of microphones. Specifically, only the uniform arrays with equally spaced microphones and sparse arrays consisting of a selection of microphones from uniform arrays are available. In this paper, based on the prolate spheroidal wave functions, we propose a grid-free compressive beamforming method compatible with arbitrary linear microphone arrays. Some examples are conducted to demonstrate the correctness and superiority of the proposed method. Plenty of Monte Carlo simulations are performed to reveal the effects of source coherence, source separation, noise, and number of snapshots. This study helps to promote the popularization and application of grid-free compressive beamforming method and offers the possibility to improve the acoustic source identification performance by optimizing microphone distribution.

Striation-Based Beamforming with Two-Dimensional Filtering for Suppressing Tonal Interference

Based on the interference spectrogram in the element–frequency domain using the data measured by the horizontal linear array, the source range can be estimated through the striation-based beamforming (SBF) method and its variants. Estimation of the striation slope is the basis for these ranging methods. But in practical scenarios, the tonal interferences and other noise make it difficult to estimate the slope. In this paper, we proposed a two-dimensional low-pass filtering method after the two-dimensional discrete Fourier transform (2D-DFT) of the element–frequency domain spectrogram. The signals can be separated from the interference and noise using this filter. For the linear frequency-modulated signal without a known waveform, we also proposed an extraction and phase compensation method based on the time–frequency spectrogram, and the acoustic data obtained can be used for source ranging. The experimental results indicate that the methods proposed are feasible.

Identification of broadband acoustical sources in the cylindric duct using a hybrid reconstruction method

Beamforming and inverse methods are widely applied to the localization and reconstruction of acoustical sources inside ducts. Identification of in-duct sources using the conventional beamforming method would lead to poor spatial resolution and even fail at some specific frequencies with multiple virtual sources. Employing the inverse method would improve the accuracy of inner duct source localization, however, severe deviation of source intensity results calculated by the inverse method from actual results would occur due to tremendous dimensions of inversion and regulation process of the transfer function matrix. To reduce the dimensions of inversion and further improve the accuracy of source identification, a combined imaging methodology is proposed. The beamforming method is firstly applied to locate regions containing sound sources preliminarily, then an inverse method is applied to regions contributed by beamforming to remove ghosts and side lobes. Numerical simulations are implemented to validate the method. Azimuthal and radial localization results indicate that the combined imaging method is with better spatial resolution than beamforming or inverse methods. Reconstructed results of the source's volumetric velocity demonstrate that the proposed method has a lower deviation of source intensity than the inverse method.

The temporal dynamics of how the brain structures natural scenes

Individuals organize the evolving stream of events in their environment by partitioning it into discrete units. Event segmentation theory (EST) provides a cognitive explanation for the process of this partitioning. Critically, the underlying time-resolved neural mechanisms are not understood, and thus a central conceptual aspect of how humans implement this central ability is missing. To gain better insight into the fundamental temporal dynamics of event segmentation, EEG oscillatory activity was measured while participants watched a narrative video and partitioned the movie into meaningful segments. Using EEG beamforming methods, we show that theta, alpha, and beta band activity in frontal, parietal, and occipital areas, as well as their interactions, reflect critical elements of the event segmentation process established by EST. In sum, we see a mechanistic temporal chain of processes that provides the neurophysiological basis for how the brain partitions and structures continuously evolving scenes and points to an integrated system that organizes the various subprocesses of event segmentation. This study thus integrates neurophysiology and cognitive theory to better understand how the human brain operates in rather variable and unpredictable situations. Therefore, it represents an important step toward studying neurophysiological dynamics in ecologically valid and naturalistic settings and, in doing so, addresses a critical gap in knowledge regarding the temporal dynamics of how the brain structures natural scenes.

ON NUMERICAL BEAMFORMING FOR CORRELATED DIPOLE TYPE SOURCES

A numerical beamforming method for correlated dipole-type sources in the frequency domain is developed. The typical configuration of the location of acoustic field sources and microphones is considered to analyze the noise generated under an aircraft flying in turbulent flow regimes. Three problems of numerical beamforming are studied, allowing one to take into account a priori information about connections between the components of the dipole function at a point. They differ in the number of real functions to be found: six (no connection), four (components are in phase) and two (the direction of the dipole moment is known). The discretization parameters of source function grids and microphones are estimated to ensure stable matrix inversion in numerical beamforming algorithms. The issues of nonuniqueness of solutions for the tangent components of the dipole function in the first and second problems are identified and discussed; for the third problem such an issue does not arise.

Frequency-domain expression of sound pressure field radiated by monopole source with general moving trajectory

Frequency-domain expressions of sound pressure field radiated by moving source were derived successfully only for some special moving trajectories in the literature. In this paper, a frequency-domain expression of sound pressure field radiated by monopole source with general moving trajectory is firstly derived. This expression has a simple mathematical form, where the sound pressure spectrum is written as a Fourier transformation of free-field frequency-domain Green functions along the trajectory. This expression is subsequently verified through theoretical and numerical verification. In theoretical verification, this expression is degenerated into the known analytical solution in each case with special moving trajectories. In numerical verification, the sound pressure spectra calculated via this expression are consistent with those calculated via a time-domain numerical method in cases without known analytical solutions. This expression is finally applied to enable the mode composition beamforming method to localize a rotating source with time-variant rotation speed.

Aerodynamics and Aeroacoustics of Leading Edge Serration in Low-Speed Axial Fans With Forward Skewed Blades

Abstract Low-speed axial fans must comply with a wide number of standards and normative restrictions, often related to the maximum noise emission levels. Among the noise control techniques in axial fans, skewed fan blades and leading edge serrations have been found to be effective in leading edge noise control, which represents one of the dominant phenomena in axial fan broadband emissions. However, these solutions are usually applied separately, and literature is scarce on systematic studies on the coupling of the two modifications. In this work, a campaign of experimental measurements was conducted on unskewed and forward-skewed axial fan blades with and without leading-edge serrations. The tests were performed in undisturbed inflow conditions. The flow field and the turbulence characteristics were measured using three-dimensional hot-wire anemometry. The suction-side sound radiation of the fans was measured with microphones in an anechoic chamber. In addition, the rotating beamforming method was used to localize the sound sources at the axial fans. It was found that, regardless of the blade skew, the leading edge serrations lead to a reduction of the sound pressure level, whereby the aerodynamic properties of the fan decrease. At the same operating points, which were achieved by adjusting the rotational speed, the sound radiation through the leading edge serrations could be reduced at high-volume flows. This effect was more pronounced with the unskewed rotor, which indicates that the positive effect of the serrations is reduced by the already optimized shape of the forward skewed fan blade. Based on the experimental results, the four geometries were further considered for numerical investigations to understand how the serrations affect the fan operations and the overall aerodynamics of the rotor. All four geometries were simulated with RANS approach at the duty point to derive a flow survey and better understand the dynamics driven by serrations and blade skewing.

Tensor-Train Decomposition-Based Hybrid Beamforming for Millimeter-Wave Massive Multiple-Input Multiple-Output/Free-Space Optics in Unmanned Aerial Vehicles with Reconfigurable Intelligent Surface Networks

Unmanned aerial vehicles (UAVs) can support low-cost, highly mobile communications while making it possible to establish dedicated terrestrial networks. To overcome the pointing error (PE) and beam misalignment of millimeter-wave large-scale multiple-input multiple-output/free-space optics (MIMO/FSO) caused by UAV jitter, a millimeter-wave massive MIMO/FSO hybrid beamforming method based on tensor train decomposition is proposed. This approach is used for reconfigurable intelligent surface (RIS) network-assisted UAV millimeter-wave massive MIMO/FSO to improve system spectral efficiency. Firstly, the high-dimensional channel of RIS-assisted millimeter-wave massive MIMO/FSO in UAV is represented as a low-dimensional channel by tensor training decomposition. Secondly, the two-way gated recursive unit attention neural network model can effectively solve the FSO PE caused by UAV jitter, and the fast fading channel and Doppler frequency shift are estimated by the Fast Cyclic Tensor Power Method (FCTPM) based on tensor training decomposition. Finally, the RIS phase shift matrix is optimized by singular value decomposition. The hybrid beamforming and RIS phase-shift matrices were estimated by using the low-complexity phase extraction alternating minimization method to solve the beam misalignment problem. Simulation results show that by using the proposed method, the spectrum utilization rate is improved by 23.6% compared with other methods.

A successive sources deletion method for enhancing the trackside acoustic weak signal of axle bearing

The traditional beamforming method has been a common tool for extracting the trackside acoustic weak signal of axle bearing and been applied to fault diagnosis. Nevertheless, considering the severe impact interference and the strong background noise, such as the rolling noise, the effectiveness of beamforming method would be poor. In order to overcome the issue, a new successive sources deletion approach based on the kurtosis and energy estimators (noted SSD-KE) is proposed for enhancing the trackside acoustic weak signal of axle bearing. Firstly, the time-domain beamforming is performed using the acoustic signal acquired by the microphone array. Then, the strong impact interference is iteratively removed in terms of a kurtosis estimator. Likewise, the energy is employed as a subsequent statistic estimator to further remove the strong background noise. Finally, the enhanced signal could be used for the fault diagnosis. The validity of SSD-KE is first examined by numerical simulations. Different configuration parameters and noise conditions are also simulated to demonstrate the superiority and robustness of SSD-KE. Furthermore, experiments considering two different types of axle bearing faults are carried out, and the obtained results emphasize that SSD-KE has better performance on weak signal enhancement and could be used for fault diagnosis under strong background noise, even with strong impact interference.

Virtual covariance matrix reconstruction-based adaptive beamforming for small aperture array.

Recently, many robust adaptive beamforming (RAB) algorithms have been proposed to improve beamforming performance when model mismatches occur. For a uniform linear array, a larger aperture array can obtain higher array gain for beamforming when the inter-sensor spacing is fixed. However, only the small aperture array can be used in the equipment limited by platform installation space, significantly weakening beamforming output performance. This paper proposes two beamforming methods for improving beamforming output in small aperture sensor arrays. The first method employs an integration algorithm that combines angular sector and gradient vector search to reconstruct the interference covariance matrix (ICM). Then, the interference-plus-noise covariance matrix (INCM) is reconstructed combined with the estimated noise power. The INCM and ICM are used to optimize the desired signal steering vector (SV) by solving a quadratically constrained quadratic programming (QCQP) problem. The second method proposes a beamforming algorithm based on a virtual extended array to increase the degree of freedom of the beamformer. First, the virtual conjugated array element is designed based on the structural characteristics of a uniform linear array, and received data at the virtual array element are obtained using a linear prediction method. Then, the extended INCM is reconstructed, and the desired signal SV is optimized using an algorithm similar to the actual array. The simulation results demonstrate the effectiveness of the proposed methods under different conditions.