Showing papers on "Adaptive beamformer published in 2019"

MISC Array: A New Sparse Array Design Achieving Increased Degrees of Freedom and Reduced Mutual Coupling Effect

Abstract: Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of direction-of-arrival estimation and adaptive beamforming. In this paper, a new sparse array configuration based on the maximum inter-element spacing constraint (MISC) is proposed. The MISC array configuration generally consists of three sparse ULAs plus two separate sensors that are appropriately placed. The MISC array configurations are designed based on the inter-element spacing set, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive closed-form expressions for the number of uniform DOFs of the MISC arrays with any number of sensors. Compared with the existing sparse arrays, the MISC array enjoys two important advantages, namely, providing a higher number of DOFs and reducing the mutual coupling effects. Numerical simulations are conducted to verify the superiority of the MISC array over other sparse arrays.

FaSNet: Low-Latency Adaptive Beamforming for Multi-Microphone Audio Processing

Abstract: 1. ABSTRACT Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called neural beamformers, have achieved significant improvements in both signal quality (e.g. signal-to-noise ratio (SNR)) and speech recognition (e.g. word error rate (WER)). Such systems are generally non-causal and require a large context for robust estimation of inter-channel features, which is impractical in applications requiring low-latency responses. In this paper, we propose filter-and-sum network (FaSNet), a time-domain, filter-based beamforming approach suitable for low-latency scenarios. FaSNet has a two-stage system design that first learns frame-level time-domain adaptive beamforming filters for a selected reference channel, and then calculate the filters for all remaining channels. The filtered outputs at all channels are summed to generate the final output. Experiments show that despite its small model size, FaSNet is able to outperform several traditional oracle beamformers with respect to scale-invariant signal-to-noise ratio (SI-SNR) in reverberant speech enhancement and separation tasks. Moreover, when trained with a frequency-domain objective function on the CHiME-3 dataset, FaSNet achieves 14.3% relative word error rate reduction (RWERR) compared with the baseline model. These results show the efficacy of FaSNet particularly in reverberant and noisy signal conditions.

Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication

Abstract: The concept of reconfigurable intelligent surface (RIS) has been proposed to change the propagation of electromagnetic waves, e.g., reflection, diffraction, and refraction. To accomplish this goal, the phase values of the discrete RIS units need to be optimized. In this paper, we consider RIS-aided millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems for both accurate positioning and high data-rate transmission. We propose an adaptive phase shifter design based on hierarchical codebooks and feedback from the mobile station (MS). The benefit of the scheme lies in that the RIS does not require deployment of any active sensors and baseband processing units. During the update process of phase shifters, the combining vector at the MS is also sequentially refined. Simulation results show the performance improvement of the proposed algorithm over the random design scheme, in terms of both positioning accuracy and data rate. Moreover, the performance converges to exhaustive search scheme even in the low signal-to-noise ratio regime.

Controllable Sparse Antenna Array for Adaptive Beamforming

Abstract: We propose an $l_{0}$ -norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an $l_{0}$ -norm penalty is used as a constraint in the CNLMS algorithm. The proposed algorithm inherits the advantages of the CNLMS algorithm in beamforming. The $l_{0}$ -norm constraint can force the quantities of antennas to a certain number to control the sparsity by selecting a suitable parameter. In addition, the proposed algorithm accelerates the convergence process compared with the existing algorithms in sparse array beamforming, and its convergence is presented in this paper. To reduce the computation burden, an approximating $l_{0}$ -norm method is employed. The performance of the proposed algorithm is analyzed through simulations for various array configurations.

Adaptive Ultrasound Beamforming using Deep Learning

Abstract: Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks that adopt the algorithmic structure and constraints of adaptive signal processing techniques can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep~learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.

Robust Adaptive Beamforming via Simplified Interference Power Estimation

Abstract: Adaptive beamformer is very sensitive to model mismatch, especially when the signal-of-interest is present in the training data. In this paper, we focus on the topic of robust adaptive beamforming (RAB) based on interference-plus-noise covariance matrix (INCM) reconstruction. First, we analyze the effectiveness of several INCM reconstruction schemes, and particularly analyze the impacts of interference power estimation on RAB. Second, according to the analysis results, we develop a simplified algorithm to estimate the interference powers, and a RAB algorithm based on INCM reconstruction is then presented. Compared with some existing methods, the proposed algorithm simplifies the interference power estimation of INCM reconstruction. Aligned with our analysis, simulation results demonstrate that the overestimation of interference powers hardly degrades the performance of adaptive beamforming, and our proposed algorithm achieves nearly optimal performance across a wide range of signal-to-noise ratios.

Deep Learning for Fast Adaptive Beamforming

Abstract: The real-time nature that makes diagnostic ultrasonography so appealing to clinicians imposes strong constraints on the computational complexity of image reconstruction algorithms. As such, these typically rely on traditional delay-and-sum beamforming, a low-complexity approach that unfortunately comes at the cost of reduced image quality as compared to more advanced and content-adaptive beamformers. Here, we propose a model-aware deep learning strategy to ultrasound image reconstruction, which leverages knowledge of minimum variance beamforming while exploiting the efficiency of deep neural networks. Our approach yields high quality images with strong contrast at real-time reconstruction rates. The neural network is trained using in vivo and simulated radio frequency channel data of a single plane wave transmit, and corresponding high-quality minimum-variance beamformed reconstructions. Performance is benchmarked using simulated acquisitions from the PICMUS [1] dataset, demonstrating the convincing generalizability and image quality of the proposed beamformer.

A 1-GHz 16-Element Four-Beam True-Time-Delay Digital Beamformer

Abstract: Phased arrays are widely used due to their low power and small area usage. However, phased arrays depend on the narrowband assumption and, therefore, are not suitable for high-bandwidth applications. Emerging communication standards require increasingly higher bandwidths for improved data rates, which results in a need for timed arrays. However, high power consumption and large area requirements are drawbacks of radio frequency (RF) timed arrays. To resolve these issues, we introduce the first true-time-delay digital beamforming IC, which eliminates beam squinting error by adopting a baseband true-time-delay technique. Furthermore, we present a constant output impedance current-steering digital-to-analog converter (DAC), which improves the spurious-free dynamic range (SFDR) of a bandpass delta–sigma modulator by 7 dB. Due to the new DAC architecture, the 16-element beamformer improves SFDR by 13.7 dB from the array. Measured error vector magnitudes (EVMs) are better than 37 dB for 5-MBd quadratic-amplitude modulation (QAM)-64, QAM-256, and QAM-512. The prototype beamformer achieves nearly ideal beam patterns for both conventional and adaptive beamforming (i.e., adaptive nulling and tapering). The difference between normalized measured beam patterns and normalized simulated beam patterns is less than 1 dB within the 3-dB beamwidth. The beamformer, including 16 bandpass analog-to-digital converters (ADCs) occupies 0.29 mm2 and consumes 453 mW in total power.

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

Abstract: In photoacoustic imaging, delay-and-sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-multiply-and-sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using the eigenspace-based minimum variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms, which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS, and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 $\text{mm}$ of the experimental images, EIBMV-DMAS results in about 113 $\text{dB}$ and 50 $\text{dB}$ sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 $\text{mm}$ , for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in signal-to-noise ratio of about 75% and 34%, compared to DMAS and EIBMV, respectively.

New Designs on MVDR Robust Adaptive Beamforming Based on Optimal Steering Vector Estimation

Abstract: The robust adaptive beamforming design problem based on estimation of the signal-of-interest (SOI) steering vector is considered in the paper. The common criteria to find the best estimate of the steering vector are the beamformer output signal-to-noise-plus-interference ratio (SINR) and output power, while the constraints assume as little as possible prior inaccurate knowledge about the SOI, the propagation media, and the antenna array. Herein, in order to find the optimal steering vector, a beamformer output power maximization problem is formulated and solved subject to a double-sided norm perturbation constraint, a similarity constraint, and a quadratic constraint that guarantees that the direction-of-arrival (DOA) of the SOI is away from the DOA region of all linear combinations of the interference steering vectors. The prior knowledge required is some allowable error norm bounds and approximate knowledge of the antenna array geometry and angular sector of the SOI. It turns out that the array output power maximization problem is a non-convex quadratically constrained quadratic programming problem with inhomogeneous constraints. However, we show that the problem is still solvable, and develop efficient algorithms for finding globally optimal estimate of the SOI steering vector. The results are generalized to the case when an ellipsoidal constraint is considered instead of the similarity constraint, and sufficient conditions for the global optimality are derived. In addition, a new quadratic constraint on the actual signal steering vector is proposed in order to improve the array performance. To validate our results, simulation examples are presented, and they demonstrate the improved performance of the new robust beamformers in terms of the output SINR as well as the output power.

FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing

Abstract: Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called \textit{neural beamformers}, have achieved significant improvements in both signal quality (e.g. signal-to-noise ratio (SNR)) and speech recognition (e.g. word error rate (WER)). Such systems are generally non-causal and require a large context for robust estimation of inter-channel features, which is impractical in applications requiring low-latency responses. In this paper, we propose filter-and-sum network (FaSNet), a time-domain, filter-based beamforming approach suitable for low-latency scenarios. FaSNet has a two-stage system design that first learns frame-level time-domain adaptive beamforming filters for a selected reference channel, and then calculate the filters for all remaining channels. The filtered outputs at all channels are summed to generate the final output. Experiments show that despite its small model size, FaSNet is able to outperform several traditional oracle beamformers with respect to scale-invariant signal-to-noise ratio (SI-SNR) in reverberant speech enhancement and separation tasks. Moreover, when trained with a frequency-domain objective function on the CHiME-3 dataset, FaSNet achieves 14.3\% relative word error rate reduction (RWERR) compared with the baseline model. These results show the efficacy of FaSNet particularly in reverberant and noisy signal conditions.

Modular Subarrayed Phased-Array Design by Means of Iterative Convex Relaxation Optimization

Abstract: Subarray design technique is widely employed in the design of planar phased arrays. However, it is hard to obtain full coverage and good radiation performance with the given shape of subarrays and array aperture. In this letter, the subarray design problem is formulated as an iterative convex relaxation model and solved using some steps subtly. Besides, the scanning sidelobe level is adopted to retrieve the optimal configuration among the found solutions. Numerical experiments are carried out to assess the performance of the proposed method as well as the inherent superiority in adaptive beamforming.

Adaptive Beamforming With Sensor Position Errors Using Covariance Matrix Construction Based on Subspace Bases Transition

Abstract: This letter proposes a narrowband interference-plus-noise covariance matrix (INCM) based beamformer, which is robust with sensor position errors for linear array. First, using the subspace fitting and subspace orthogonality techniques, we estimate a set of angle-related bases for the signal-plus-interference subspace (SIS) by solving a joint optimization problem. Second, we obtain the bases transition matrix between the estimated angle-related bases and the orthogonal bases consisting of the dominant eigenvectors of the sample covariance matrix (SCM). The SCM can be expressed as a function of the angle-related bases and the bases transition matrix. We construct the INCM directly from the SIS by eliminating the component of the desired signal from the angle-related bases. Simulations and experimental results show that the proposed beamformer outperforms other tested beamformers in the presence of sensor position errors.

Robust Wideband Adaptive Beamforming With Null Broadening and Constant Beamwidth

Abstract: In the cases of rapidly moving interference and look-direction error, robustness and output signal to interference and noise ratio (SINR) of conventional wideband adaptive beamforming methods are degraded severely. In order to solve these problems, a robust wideband adaptive beamforming with null broadening and constant beamwidth is proposed. We first propose a taper matrix constructed by adding virtual interferences for null broadening, where the reference frequency and number of virtual interferences of taper matrix are determined based on numerical methods. This is followed by reconstructing the array covariance matrix based on taper matrix. Spatial response variation (SRV) constraint is then applied for a constant beamwidth over the entire frequency band. Finally, the adaptive weight vector for null broadening and constant beamwidth is obtained by using Lagrange multiplier method. The effectiveness of proposed method is verified by numerical simulations.

High Frequency Over-the-Horizon Radar

Abstract: Presents a collection of slides covering the following topics: high frequency over-the-horizon radar; HF channel model; time-varying adaptive beamforming; space-time adaptive processing; GLRT detection schemes; HF passive radar; blind signal separation; multipath-driven geolocation and MIMO OTHR concept.

Adaptive Beamforming With Sidelobe Suppression by Placing Extra Radiation Pattern Nulls

Abstract: A new iterative adaptive beamforming (ABF) algorithm based on conventional beamformers is proposed in order not only to steer the main lobe toward the desired signal and place radiation pattern nulls toward respective interference signals but also to achieve the desired sidelobe level (SLL). Thus, the algorithm becomes less susceptible to unpredicted interference signals than conventional beamformers. In each iteration, the algorithm finds the direction of the peak of the greatest sidelobe, which is considered as direction of arrival (DoA) of a hypothetical interference signal, and the conventional beamformer is then employed to find proper antenna array weights that produce an extra null toward this direction. The iterative procedure stops when the desired SLL is obtained. The algorithm is applied on three conventional beamformers and is tested for various signal DoA, while the direction deviation of the main lobe and the nulls is recorded, to evaluate the algorithm in terms of robustness. The proposed algorithm needs a few iterations to achieve the desired SLL and thus is much faster than any evolutionary iterative method employed for sidelobe suppression. Finally, unlike methods that employ neural networks (NNs), the proposed algorithm does not need any training to become functional.

A new computing paradigm for the optimization of parameters in adaptive beamforming using fractional processing

Abstract: The use of fractional calculus based novel adaptive algorithms to solve various applied physics and engineering problems is an emerging area of research. In the present study, the parameter estimation problem in adaptive beamforming is explored through the fractional least mean square (FrLMS) adaptive algorithm. The FrLMS algorithm uses the concept of the fractional order gradient in addition to the standard integer order gradient calculation in the recursive parameter update mechanism of optimization. The unknown parameters of adaptive beamforming networks are effectively estimated using FrLMS for various scenarios based on the number of antenna elements in a uniform linear array, the number of interference signals, the signal to noise ratios (SNRs) as well as fractional orders. A comparative study of the proposed FrLMS with standard LMS for different scenarios of adaptive beamforming shows the quality of the design scheme in terms of accuracy, convergence, robustness and stability.

Resolving Ultrasound Contrast Microbubbles Using Minimum Variance Beamforming

Abstract: Minimum Variance (MV) beamforming is known to improve the lateral resolution of ultrasound images and enhance the separation of isolated point scatterers. This paper aims to evaluate the adaptive beamformer’s performance with flowing microbubbles (MBs) which are relevant to super-resolution ultrasound imaging. Simulations using point scatterer data from single emissions were complemented by an experimental investigation performed using a capillary tube phantom and the Synthetic Aperture Real-time Ultrasound System (SARUS). The MV performance was assessed by the minimum distance that allows the display of two scatterers positioned side-by-side, the lateral Full-Width-at-Half-Maximum (FWHM), and the Peak-Sidelobe-Level (PSL). In the tube, scatterer responses separated by down to $196~\mu \text{m}$ (or $1.05\lambda $ ) were distinguished by the MV method, while the standard Delay-And-Sum (DAS) beamformers were unable to achieve such separation. Up to ninefold FWHM decrease was also measured in favor of the MV beamformer for individual echoes from MBs. The lateral distance between two scatterers impacted on their FWHM value, and additional differences in the scatterers’ axial or out-of-plane position also impacted on their size and appearance. The simulation and experimental results were in agreement in terms of lateral resolution. The point scatterer study showed that the proposed MV imaging scheme provided clear resolution benefits compared to DAS. Current super-resolution methods mainly depend on DAS beamformers. Instead, the use of the MV method may provide a larger number of detected, and potentially better localized, MB scatterers.

Deep Learning-Based Universal Beamformer for Ultrasound Imaging

Abstract: In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.

OPARC: Optimal and Precise Array Response Control Algorithm—Part II: Multi-Points and Applications

Abstract: In this paper, the optimal and precise array response control (OPARC) algorithm proposed in Part I of this two paper series is extended from single point to multi-points. Two computationally attractive parameter determination approaches are provided to maximize the array gain under certain constraints. In addition, the applications of the multi-point OPARC algorithm to array signal processing are studied. It is applied to realize array pattern synthesis (including the general array case and the large array case), multi-constraint adaptive beamforming, and quiescent pattern control, where an innovative concept of normalized covariance matrix loading is proposed. Finally, simulation results are presented to validate the effectiveness and good performance of the multi-point OPARC algorithm.

Fast-Moving Jamming Suppression for UAV Navigation: A Minimum Dispersion Distortionless Response Beamforming Approach

Abstract: Unmanned aerial vehicle (UAV) has been widely used in various fields, which mainly relies on global navigation satellite system (GNSS) to obtain position information. However, fast-moving jamming poses a big challenge for UAV navigation. This motivates us to develop new robust adaptive beamforming technique that is capable of enhancing the navigation signal and suppressing the jamming efficiently. Since the navigation signal and jamming present non-Gaussianity in statistics, the minimum dispersion distortionless response (MDDR) beamformer is utilized, which exhibits better performance of non-Gaussian signal reception over the minimum variance-based beamformer. To suppress the fast-moving jamming, the minimum power constraint is added to the optimization problem regarding to the MDDR beamformer, leading to a resultant problem with a quadratic constraint. Furthermore, instead of the quadratic constraint, a linear version is derived under the power constraint to achieve a shape null sector towards the fast-moving jamming, and as a consequence, a linearly constrained MDDR beamforming is established. This allows us to develop two algorithms, i.e., quasi-MVDR algorithm and Newton's method, to accelerate the solution. Simulation results show that the proposed beamformer is able to suppress the fast-moving jamming efficiently for UAV navigation.

Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming

Abstract: Recently, a number of robust adaptive beamforming (RAB) methods based on Capon power spectrum estimator integrated over a specific region for covariance matrix reconstruction have been proposed. However, all of these methods ignore the residual noise existing in the Capon spectrum estimator, which results in reconstruction errors. In this paper, we propose a RAB algorithm via residual noise elimination and interference powers estimation to reconstruct covariance matrix. First, the proposed algorithm demonstrates the existence of residual noise and analyze its relationship to actual noise. Then, after eliminating the residual noise, the modified Capon power spectrum estimator is utilized to reconstruct the covariance matrix and desired signal SV. Moreover, to reduce the influence of the desired signal on interference powers estimation, we project the snapshots onto the complementary subspace of the desired signal and estimated interference powers are derived according to the theoretical formulation of the interference covariance matrix (ICM). The simulation results demonstrate that the proposed method is robust against various mismatches and can achieve superior performance.

ADS-B Signal Separation Based On Blind Adaptive Beamforming

Abstract: The automatic dependent surveillance-broadcast (ADS-B) is a surveillance system for air traffic management where aircrafts asynchronously broadcast the position and other information on the same frequency band. However, this simple transmission protocol will result in inevitable overlapping among multiple ADS-B signals. In this paper, with the inherent characteristics of the ADS-B signal, an algorithm is proposed to separate the overlapping ADS-B signals. First, a nonconvex blind adaptive beamforming problem is formulated where the constraints are different from those in common beamforming algorithms. Especially, for the non-overlapping snapshot, the relationship between the output of the beamformer and that of the first array element is constrained. After that, the alternating direction method of multipliers algorithm is utilized to solve the nonconvex blind adaptive beamforming problem. Simulations demonstrate that the proposed algorithm is effective even if there is only one non-overlapping snapshot.

Weighted Subspace-Constrained Adaptive Beamforming for Sidelobe Control

Abstract: A weighted subspace-constrained minimum variance distortion-less response (WS-MVDR) beamformer is proposed for robustly controlling the peak sidelobe level (PSL) as well as the sidelobe shape of adaptive array pattern with stable mainlobe. The adaptive weight vector is projected onto the subspace constructed from the weighted sidelobe steering vectors. The norm of the projection is constrained below the derived constant value determined by the desired PSL. The only inequality constraint is introduced to the conventional MVDR beamformer, resulting in a second-order cone programming problem. The numerical results demonstrate that WS-MVDR outperforms existing ones in terms of beampattern control capability and computational complexity.

Middle Subarray Interference Covariance Matrix Reconstruction Approach for Robust Adaptive Beamforming With Mutual Coupling

Abstract: In this letter, a middle subarray interference-plus-noise covariance matrix (INCM) reconstruction approach is proposed to mitigate the mutual coupling problem in robust adaptive beamforming. In the proposed approach, the banded symmetric Toeplitz structure of mutual coupling matrix in the uniform linear array is employed. The INCM of middle subarray is first reconstructed by using the Capon spectrum of middle subarray to integrate over the possible interference region. Similarly, the desired signal covariance matrix of middle array is calculated over the desired signal region, and the desired signal steering vector of middle array is estimated. Finally, the proposed middle subarray beamformer weight vector is obtained. Simulation results validate the superiority and effectiveness of the proposed method.