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The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of $76.21\pm 3.77$%.

Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.

Multiple-input multiple-output (MIMO) Minimum mean-square error (MMSE) receivers are widely adopted in the latest communication standards and reducing the complexity of these receivers while preserving the error performance is highly desirable. In this work, we study the error performance and implementation complexity of MIMO MMSE receivers when combined with a coordinate interleaved signal space diversity (SSD) technique. Contrary to the well-known trade-off between the error performance and implementation complexity, the proposed system leads to a considerably simplified MIMO MMSE receiver with significant performance gains when compared to the original MIMO MMSE receiver. Unlike the standard MIMO MMSE receiver, the proposed coordinate interleaved technique induces a block diagonal transmit correlation matrix providing both performance enhancement and complexity reduction. The results show that the error performance can be improved more than 10dB with up to 71% computational complexity reduction. The complexity comparison between the original and proposed approaches is also verified by means of field-programmable gate array (FPGA) implementation.

In this paper, we investigate non-orthogonal multiple access (NOMA) scheme in millimeter wave (mmWave) communication to serve nonclustered multiple users. We explore a low complex design of an analog beamforming weight vector and the power requirement of users aiming to minimize the total power targeting to satisfy the spectral efficiency (SE) requirements of all users. We propose a low complex constant modulus analog beamforming (CMAB) algorithm, where we first reduce the number of signal to interference plus noise ratio (SINR) constraints, which is attained from the order of equivalent channel gain of users. Then, the nonconvex constraint of constant modulus (CM) is relaxed and semi-definite programming (SDP) is used to solve the problem. Obtained weight vector and power for all users are optimal since the rank of positive semi-definite (PSD) matrix is one. Later, CM constraint is included. Simulation results show that the proposed algorithm requires less power with minimum complexity compared to the existing research, digital beamforming NOMA and time division multiple access (TDMA) for the same SE requirements.

In the highly cluttered undersea environment, sonar array systems require enhanced acoustic signal processing algorithms and sophisticated architectures in order to meet dependability and real-time mission requirements. The probability of hydrophone and processing element failures is very high in such severe operating environments. Adaptive matched-field processing (MFP) algorithms localize sources accurately with moderate levels of signal-to-noise ratio (SNR) and precise knowledge about environments by employing full-wave acoustic propagation models. However, they highly distort output beam patterns with significant increase of sidelobes in the presence of environmental mismatches and element failures. These problems make the development of advanced fault-tolerant signal processing algorithms imperative to tolerate the element failures in cases where replacement of defective elements is impossible or impractical. In this paper, three fault-tolerant MFP algorithms are presented to compensate for the performance degradation generated by the inherent failure characteristics of vertical line arrays. The beamforming performance and computational complexities for these fault-tolerant algorithms are analyzed in terms of the number of faulty elements, their positions in the array, and SNRs. The simulation results demonstrate that these fault-tolerant techniques provide a feasible solution for real-time and highly reliable beamforming implementation on sonar array systems.

A study for applying data extrapolation technique based on wave superposition method (WSM) is proposed to overcome the disadvantages of near-field acoustical holography (NAH). Unlike conformal NAH, where the measurement surface surrounds the entire structure, in the patch holography the measurement surface needs only be approximately as large as the patch on the structure surface where the reconstruction is required. At first, a microphone array is used to acquire the sound pressure field radiated from a vibrator; then a beamforming method is adopted to locate sound sources; after that, a serial of equivalent sources are collocated around these sound sources; at last, a data extrapolation technique based on WSM is applied to extend the measurement aperture and reconstruct the sound field. Since the data extrapolation algorithm requires the inversion of Green's function matrix which may be ill-conditioned, Tikhonov regularization method is used to invert it, and the value of the regularization parameter is determined by the L-curve criteria. The effectiveness of this method is demonstrated by numerical simulation with two pulse ball model, and also experiment is carried out in a semi-anechoic chamber using two sound boxes. The results confirm that the exterior sound field can be accurately reconstructed with few iterative times and the reconstruction error is sufficiently suppressed by the proposed method.

Due to the growing number of drones and unmanned aerial vehicles, the aeroacoustic noise radiated by airfoils in moderate Reynolds-number flows increasingly becomes the focus of interest. In the present paper, the flow-generated noise of two basic (a flat plate and a cambered plate) and two conventional (RAF6 and NACA 0012) airfoil profiles is investigated in an open-jet wind tunnel in the interval of Reynolds numbers between 60,000 and 140,000. The measurements were carried out with a phased array microphone. The measurement data were evaluated using frequency–domain beamforming and the analysis of variance statistical method. The results show that the emitted noise is nearly independent of the angle of attack but dependent on the airfoil geometry and the chordwise location.

In this paper, we present simulations and experimentally collected bird song data collected using a modified Voxnet acoustic array node (with four microphones) to perform 3D direction-of-arrival (DOA) estimation of various bird sources. We used the Approximate Maximum-Likelihood (AML) algorithm to construct the steering matrix in the beamforming process for the estimation of the DOA of the bird signals. While the computational burden is high in the 3D scenario, various strategies have been developed to reduce the computational burden of the algorithm for potential real-time applications. Extensive simulations and experimentally collected data are used to validate the effectiveness of the AML algorithm for 3D estimations and the usefulness of the modified Voxnet node. Both the estimated azimuths and elevations have approximately plus and minus 10 degrees of errors.

Due to the dispersion of normal modes in shallow water, there exist regular striations on the interference spectrogram in space-frequency domain. Based on the range-frequency domain striations, waveguide invariant can be estimated with the prior knowledge of source range. Utilizing striations along the array, Rouseff and Zurk [J. Acoust. Soc. Amer.130 (2011) EL76–EL81] proposed the striation-based beamforming (SBF), which shows a theoretical possibility to estimate waveguide invariant that is independent of source range. To make this beamforming technique more practical, its application requirements and performance limitation should be defined. These two issues are researched with the phase and amplitude relationships deduced by the frequency-shift compensation theory. It shows the source whose spectrum varies slowly and received signals that contain the complete information of transmission time are keys for applying SBF to real work. The azimuth limitation can lead to the degradation of array gain and distortion of interference structure. Matching parameters of waveguide invariant and source range, respectively, two modified striation-based beamformers are developed to eliminate the azimuth limitation. Simulation and experimental results are presented.

In shallow water, there exist regular interference striations on the structured sound intensity pattern versus frequency and element location of a horizontal array. Beamforming along the striation with frequency-shifted pressure can improve the correlation among array signals, but the beam focusing performance is limited by the source spectrum (e.g. random phase spectrum generated from ship-radiated noise) and signal truncation. In this paper, the cross-correlation phase relationship among the array signals is deduced, and striation-based beamforming with such cross-correlated data can produce a similar directivity to the conventional beamforming, while the effect from signal truncation and source spectrum can be eliminated. This striation-correlation-based beamforming also outputs the signal corresponding to the Green’s function between the source and the reference array element, moreover, recovers the interference structure that characters the channel. The striation-based array signal processing also provides the proposed Greens function estimation method with the ability to work effectively under the condition of low signal-to-noise ratio. Simulation results are presented and performance variations related to signal frequency, signal-to-noise ratio, striation slope estimation error, element number and array aperture are also analyzed numerically.

In this work, an algorithm that combines deep-learning based dereverberation and beamforming is proposed for sound source localization in the reverberant environment. The contribution is combining deep-learning based dereverberation and beamforming together. Through deep learning, the proposed algorithm can directly achieve dereverberation of the cross-spectral matrix of microphone array measurement signals in the reverberant environment, which can overcome the challenge of requiring multiple measurements like average beamforming. In this way, the proposed algorithm can be applied to the reverberant environment that requires efficient sound source localization. At the same time, the network of deep-learning based dereverberation is trained directly using the cross-spectral matrix of signals collected by microphone arrays, which can overcome the challenge of requiring prior knowledge of reflective surfaces like empirical dereverberation beamforming. In this way, the proposed algorithm can be applied to the reverberant environment composed of walls with unknown sound reflection coefficients. Both specular reflections, diffuse reflections, and background noise are considered in the reverberant environment. The cross-spectral matrix of microphone array measurement signals in the reverberant environment is first fed into the U-net for dereverberation and then combined with beamforming for sound source localization. The results in the test set show that the proposed algorithm can eliminate the influence of reverberation on sound source localization. The applicability limitation results of the proposed algorithm show the proposed algorithm’s strong robustness to unseen sound reflection coefficient, unseen SNR, and expandability to some degree for unseen source location. However, the proposed algorithm cannot provide stable and accurate sound source localization results under unseen room geometry. The proposed algorithm has higher spatial resolution and smaller errors than the average beamforming and the empirical dereverberation beamforming.

It is evident that the electroencephalogram (EEG) rhythms are slightly changed when the efficacy of mental activity declines (brain fatigue). Nonetheless, this slight change is not easily detectable by the so far suggested scalp EEG features. The goal of this paper is to propose an EEG-based biomarker, which has a congruity to the mental fatigue variation to detect the transition from non-fatigue to the fatigue mental state. The strength of the dominant EEG source, extracted by minimum variance beamformer (MVB), is proposed here as a discriminative feature to remarkably classify the two mental states. To assess the proposed scheme, EEG signals of 17 volunteers were recorded via 32 electrodes before and after taking an exhausting mental exam (3h) and the extracted EEG features were labeled as non-fatigue and fatigue, respectively. After removing the eye-blink effect, the proposed feature along with the conventional EEG features were extracted from the recorded EEGs and then applied to support vector machine (SVM) and 1-nearest neighbor (1NN) classifiers in order to differentiate these two mental states. The best result is achieved by applying the proposed feature to the SVM classifier providing 97.06% classification accuracy which is significantly ($p<0.05$) superior to its counter parts.

For Global Navigation Satellite System (GNSS), adaptive beamforming techniques are critical to interference and multipath mitigation in various applications. The major problems with existing beamforming architectures are their Initial Measurement Unit (IMU) dependence and need for gain/phase calibration. In this paper, we proposed an optimal blind beamforming architecture that could achieve the maximum output signal-to-interference-plus-noise ratio (SINR) in the presence of gain/phase mismatch. The effectiveness of the proposed architecture is verified by theory analysis and simulation results.