Beamforming Method Research Articles

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses

Background and objectives:The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task. Methods:For OPM-MEG, the β-band (15–30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed. Results:The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent. Conclusions:Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

Sparse microphone array design for frequency-invariant beamforming via group [formula omitted]-norm optimization

Frequency-invariant (FI) beamforming aims at recovering signals without any distortion and maintaining spatial selectivity over the entire bandwidth. However, most existing FI beamforming (FIB)methods consider the weighted ℓ1-norm or modified ℓ1-norm of the filters as the objective function for sparse FIB design, which cannot assure the optimal sparse solution. To deal with this drawback, an optimization problem for the FIB design of sparse microphone array in terms of ℓp-norm (0<p<1) minimization is formulated, where distortion-less response, mainlobe and sidelobe constraints are considered. Due to the existence of the ℓp-norm objective function, the resultant problem is nonconvex, and therefore, an alternating direction method of multipliers (ADMM) algorithm is devised. Specifically, the corresponding problem is decomposed into multi-block subproblems to determine all unknown variables separately. Then, the most dominant sensor positions for each frequency are determined by using the principal component analysis (PCA) and K-mean clustering algorithm. Numerical examples show that the proposed design achieves good directivity factor, frequency invariance and better sparsity.

High-resolution sub-bottom profiling technology using parametric array and vector hydrophone

This paper reports a new method to achieve high-resolution sub-bottom profiling, providing a scheme for the development of Parametric Array Sub-bottom Profiler (PSBP). The high-resolution sub-bottom profiling system is integrated using parametric transmitting array and vector hydrophone, series signal enhancement and high-resolution signal processing methods are proposed to obtain clear sub-bottom profiles. Based on the parametric array, a small-aperture high directivity beam with low frequency, broadband, and without sidelobes is realized, physically achieved high spatial resolution and temporal resolution. By using the vector hydrophone, a single side frequency doubling beamforming (SSFDB) method is used to significantly suppresses the impact of marine environmental noise and sea surface acoustic scattering, the detection resolution is improved due to the frequency doubling characteristics of SSFDB at the same time. Low order bidirectional differential arithmetic unit (LOBDAU) and coherent accumulator (CA) are combined to enhance the echo signal, and finally the high-resolution layering method (HRLM) based on peak detection gives a clear sub-bottom profile of the sediment. The benefits of the combined acoustic array design scheme and the researched signal processing methods had been experimentally evaluated, a clear sub-bottom profile with a semi buried sunken ship was obtained.

Electromagnetic noise characteristics analysis and noise traceability in induction motor

Abstract To investigate the electromagnetic vibration and noise issues in induction motors, this study employs a combined methodology of experimental, theoretical, and simulation approaches to analyze the characteristics of electromagnetic noise. Initially, a theoretical analysis of the radial electromagnetic force of the motor is conducted, followed by a simulation of the motor’s electromagnetic vibration and noise at a working condition of 3000 rpm, incorporating a Multiphysics coupled analysis of electromagnetic-vibration-noise. Vibration testing on a specific compressor motor reveals that the primary vibration noise characteristic frequency is centered around 2000 Hz. A comparison between simulation and experimental results shows a high degree of congruence, indicating the reliability of the simulation method. Subsequently, an acoustic camera kit, Brüel &amp; Kjær PULSE Reflex, based on a beamforming method, is employed for noise source identification and acoustic holography testing at 3000 rpm, which successfully localizes the noise sources. The analysis and experiments provided herein can serve as a reference for the development of electromagnetic vibration noise suppression techniques in induction motors.

Frame rate improvement in coherent plane-wave compounding using null subtraction imaging

Coherent plane-wave compounding (CPWC) has attracted significant attention in preclinical research and clinical diagnosis. However, a trade-off exists in CPWC between the image quality and frame rate. Null subtraction imaging (NSI), as a novel beamforming method, can realize high-quality imaging with enhanced lateral resolution and suppressed grating lobes. This study explores NSI to reduce the number of emissions while preserving the imaging quality. The performance of the proposed method is assessed using the Plane-Wave Imaging Challenge in Medical Ultrasound datasets, including phantom and in vivo datasets. The results verify the potential of NSI in terms of frame rate improvement compared to the conventional delay and sum beamforming method. NSI with 5 and 15 plane-wave emissions is evaluated. For example, for the experimental phantom datasets, NSI with 15 plane-wave emissions improves the lateral full-width at half maximum by 0.10 in comparison to DAS with 75 plane-wave emissions. Moreover, the corresponding contrast ratio and generalized contrast-to-noise ratio increase from 25.11 to 31.19 dB and 0.63 to 0.68, respectively, indicating contrast improvement. Thus, compared to DAS with 75 plane-wave emissions, the proposed method accelerates data acquisition by a factor of 5, resulting in remarkable frame rate improvement.

High‐resolution 3D imaging by microwave photonic time division multiplexing‐multiple‐input‐multiple‐output radar with broadband digital beamforming

AbstractA broadband microwave photonic time division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) radar is proposed in which photonic frequency quadrupling is adopted to generate broadband radar signals and photonic frequency mixing is implemented for de‐chirping processing of radar echoes. By utilising two radio frequency switches to control the signal transmission and reception, TDM‐MIMO mechanism is formed using a single microwave photonic radar transceiver. This microwave photonic TDM‐MIMO radar not only achieves high range resolution using broadband processing but also enables high angular resolution and forward‐looking imaging capability with low system complexity. Besides, a broadband digital beamforming (DBF) method is introduced to solve the broadband beam squint and broadening problems and implement near‐field correction. In the experiment, a microwave photonic TDM‐MIMO radar with an 8×8 T‐shape antenna array is established with a bandwidth of 8 GHz (18–26 GHz) in each channel. The range and angular resolutions are estimated to be ∼2 cm and ∼2°, respectively. Applying the broadband DBF method, high‐resolution 3D imaging of small targets is achieved with good focusing of targets and deep suppression of grating lobes and side lobes. Hence, the proposed microwave photonic TDM‐MIMO radar with broadband DBF provides a promising solution for high‐resolution 3D imaging.

Neural oscillations guiding action during effects imagery

Goal-directed acting requires the integration of sensory information but can also be performed without direct sensory input. Examples of this can be found in sports and can be conceptualized by feedforward processes. There is, however, still a lack of understanding of the temporal neural dynamics and neuroanatomical structures involved in such processes. In the current study, we used EEG beamforming methods and examined 37 healthy participants in two well-controlled experiments varying the necessity of anticipatory processes during goal-directed action. We found that alpha and beta activity in the medial and posterior cingulate cortex enabled feedforward predictions about the position of an object based on the latest sensorimotor state. On this basis, theta band activity seems more related to sensorimotor representations, while beta band activity would be more involved in setting up the structure of the neural representations themselves. Alpha band activity in sensory cortices reflects an intensified gating of the anticipated perceptual consequences of the to-be-executed action. Together, the findings indicate that goal-directed acting through the anticipation of the predicted state of an effector is based on accompanying processes in multiple frequency bands in midcingulate and sensory brain regions.

Acoustic wind tunnel experimental study of aerodynamic noise distribution in high-rise buildings

Wind-induced aerodynamic disturbance has become a major noise source affecting residents in super high-rise buildings. To investigate the distribution characteristics of aerodynamic noise on high-rise building surfaces in strong wind conditions and the impact of wind speed and direction, this paper pioneers the application of the acoustic wind tunnel method based on microphone array localization to the experimental study of the surface acoustic pressure field of a high-rise building model, the far-field noise spectrum of which is obtained using the microphone array sensor. The beamforming calculation method was used to obtain the spectrum and localization cloud. By comparing the spectra and cloud diagrams under different process conditions and based on existing building wind pressure reports, it is concluded that: the noise source is always located at the height of the connecting corridor where there is large wind pressure pulsation in the two building models; the aerodynamic noise is more pronounced around building gaps where the airflow is more disturbed and also in the oncoming wind trailing direction where the wind pressure pulsation is more pronounced. An increase in wind speed increased noise source amplitude from 30 dB to 60 dB, and the location of the noise source shifted backwards to the tail of the incoming wind. This paper shows that the acoustic wind tunnel method based on microphone array localization technology can obtain the aerodynamic noise intensity on the surfaces of super high-rise buildings and reasonably predict the location of the sound source. This article puts forward suggestions to reduce noise in high-rise buildings based on experimental results.

Simple subspace based adaptive beamforming under Toeplitz covariances

When uncorrelated signals are incident on a uniform linear array, the array covariance matrix is of the Toeplitz form. An adaptive beamforming method is proposed based on the signal-plus-interference (SI) subspace via the Toeplitz rectification of the sample matrix. The rectified matrix is shown to be more accurate in a norm sense than the modified matrix according to the centro-Hermitian property. Since the former also is centro-Hermitian we can efficiently obtain its eigen-decomposition from a real matrix and then the weight vector in the estimated SI subspace. The proposed method, showing robustness to pointing errors, is not only computationally efficient but also very quickly converges to the optimum performance as demonstrated in the simulation.

Robustness analysis and experimental validation of a deep neural network for acoustic source imaging

Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model.

Frame-wise speech extraction with recursive expectation maximization for partially deformable microphone arrays

Large-apertured wearable and deformable microphone arrays have shown their better performance than conventional small wearable and rigid microphone arrays for speech extraction tasks, especially when the desired speech is far away from the arrays or there exist a great number of directional noises in practical applications. Most existing beamforming methods that require the information of the desired sound direction often cannot be used directly on deformable arrays because their shape might continuously change over time. To tackle the problem of utilizing partially deformable microphone arrays for target speech extraction, this paper derives a frame-wise speech extraction method, which is referred to as the recursive multichannel Wiener filter with generalized Gamma priors. In this method, the complex spectrum of the desired speech is assumed to follow a zero-mean complex Gaussian distribution with time-varying variance, and the reciprocal of this time-varying variance known as the precision is further assumed to follow a Gamma distribution, while the complex spectrum of the noise is assumed to follow a zero-mean complex Gaussian distribution. Under these two assumptions, we derive the recursive expectation maximization algorithm to extract the desired speech in a frame-by-frame manner. Experimental results show the promising performance of the proposed method in terms of multiple objective metrics.

Research on Azimuth DBF Method of HRWS SPC MAB SAR Imaging Mode with Non-Ideal Antenna Mode

Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise because the inter-beam interference is not considered. When the real antenna mode is inconsistent with the ideal one, the disadvantages of the combination scheme become more serious. In this paper, based on the basic theory of the low-pass, band-limited, multiple-channel under-sampling and reconstruction, a novel digital beam-forming method is proposed for the SPC MAB imaging mode with ideal antenna mode first. The method analyzes the system functions of the sub-beams, based on which digital beam-forming filters are designed for all the sub-beams. The designed filters can reconstruct the correct wide-bandwidth azimuth spectrum and suppress the inter-beam interference simultaneously. Furthermore, the proposed method is extended to SPC MAB mode with the non-ideal antenna mode. The simulation experiments prove the validities of the proposed method both for azimuth spectral reconstruction and the inter-beams interfering suppressing, no matter that the SPC MAB’s antenna mode is ideal or non-ideal.

Localizing aeroacoustic sources in complex geometries: A hybrid method coupling 3D microphone array and time-reversal

Flow-induced noise is widely encountered in engineering domains, and noise source localization is the first step to understand the generation mechanisms. A large part of aeroacoustic noise measurements is performed in wind tunnels by using microphone arrays. A widespread method to identify the noise sources based on these measurements is the beamforming technique. This relies on a Green function, which ideally accounts for source type, flow, and reflecting boundaries. This function is generally not known analytically. An alternative approach is the time-reversal technique considered here. This method consists in propagating the sound field from the microphones back to focus points interpreted as sources. The back propagation can be done numerically with an acoustic propagation solver, which replaces the Green function needed in the beamforming. To date time reversal has been used to localize sound sources with flow in two-dimensions, without considering any reflecting/diffracting boundary. The objective of this work is to consider a three-dimensional situation with flow, and to include the presence of such boundaries to see if this can improve source localization. A synthetic sound source enclosed in a streamlined body is placed in a wind tunnel, and its sound field is diffracted by a nearby airfoil. A three-dimensional antenna with 768 microphones is used to measure the sound field, and time-reversal is performed by solving the three-dimensional linearized Euler equations with a discontinuous Galerkin method. The interest of taking into account the airfoil geometry for source localization is discussed, and some comparisons with the beamforming method are performed.

An acoustic imaging recognition based cutting tools wear state prediction method

Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach.

Near-field beamforming method based on motion model analysis for UAVs communication

This study presents a near-field beamforming method for the moving swarm composed of unmanned aerial vehicles (UAVs) to address the challenges of position errors that hinder effective beamforming. Firstly, a model with positional errors in moving UAVs was developed. The sources and characteristics of UAVs’ position errors were analyzed and classified into low-frequency motion errors and high-frequency vibration errors, and their effects on UAVs array beamforming were analyzed using coordinate transformation and decomposition. Secondly, a parameter estimation algorithm was proposed based on the Fast Fourier Transformation (FFT) and the Least Square (LS) method to estimate the unknown parameters of the UAV's low-frequency motion errors. Lastly, a near-field beamforming algorithm for UAVs was proposed based on the Interpolation Kalman filter (IKF). The effectiveness of the proposed model and algorithms was verified through simulations, which provided a theoretical basis for the near-field beamforming under the existence of the UAVs position errors.

Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks

Conventional beamforming methods for reconfigurable reflector antennas assume full control over the amplitude and phase of the reflected field. Here, we develop a novel beamforming methodology for reflecting Programmable Metasurfaces (PMS) with capacitive memory. Although utilizing such fully reactive PMS simplifies antenna design and reduces energy consumption, the PMS reflection magnitude is unity and thus a global optimization of the reflection phases over the PMS unit cells is required in each beamforming scenario. We propose an implementation of such an optimization method rooted in the traditional Fourier transform-based beamforming and evaluate its performance. Additionally, we show that a pair of trained feed-forward neural networks (FFNN) with one input, one hidden, and one output layer can replace time-consuming global optimizations in the case of a PMS comprising 3×10 unit cells. We train the FFNNs on a dataset obtained for typical single- and dual-beam beamforming scenarios. After training, the FFNNs perform requested beamforming tasks within a fraction of second and with about the same accuracy as the original optimization algorithm. The proposed methodology may find applications in future mobile telecommunication systems that require real-time beamforming on low-end hardware. The same beamforming methodology can be also employed in short-range wireless power transfer systems.

A short wave radar beam sharpening method based on generalised oblique projection operator with flexible parameter

AbstractBeamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters’ outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam.

Realization of adaptive beamformer on open-source hardware

Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient.

Performance analysis of power-efficient IRS-Assisted full duplex NOMA systems

In this paper, we utilize intelligent reflecting surfaces (IRSs) in full-duplex (FD) systems to enhance diversity gain and system performance. The issue of self-interference in FD systems causing an outage floor is addressed. We propose a beamforming (BF) method that mitigates this effect, even when there is no direct channel between the source and the far user in non-orthogonal multiple access (NOMA) systems. We examine two designs, named fixed antenna mode (FAM) and adaptive antenna mode (AAM), for K decode-and-forward (DF) FD near users in a Rayleigh fading environment. To improve system performance, we employ joint antenna mode and near user selection techniques in IRS-NOMA systems. We evaluate the performance of the proposed schemes and derive closed-form expressions for outage probability and ergodic capacity, which are validated through simulations. The results demonstrate that the proposed scheme achieves additional spatial diversity gain through the use of the IRS structure and antenna mode selection at the relay nodes. Furthermore, the new BF technique improves power efficiency compared to conventional FD NOMA structures, and the analysis can be extended to 6G Massive MIMO systems using machine learning algorithms, which are applicable for millimeter wave channels.