Showing papers on "Adaptive beamformer published in 2021"

Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation

Abstract: This article studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments.

Adaptive Beamforming for Multi-Function In-Band Full-Duplex Applications

Abstract: The ability of in-band full duplex (IBFD) technology to support multiple simultaneous functions within wireless systems will enable it to be adopted into both existing networks and innovative applications that have not yet been fully realized. In order to accomplish this goal, an effective way to mitigate the consequential self-interference must be employed. One of the many techniques considered to do this is adaptive beamforming for IBFD designs with antenna arrays, which offers untapped potential. This article provides background on beamforming approaches that have been used within IBFD systems, and presents a survey of published results to quantify the performance of these methods. Additionally, the design of two of the highest-isolation prototypes are discussed within the context of providing practical examples for both omnidirectional and directional architectures. Overall, this conversation highlights the use of adaptive beamforming within IBFD systems, and illustrates how it is poised to become more prevalent within future wireless networks.

A Reinforcement Learning Based Approach for Multitarget Detection in Massive MIMO Radar

Abstract: This article considers the problem of multitarget detection for massive multiple input multiple output cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multitarget detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_{\mathsf {D}}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamformin optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL-based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance, and rapidly changing scenarios.

Reconfigurable Intelligent Surface-Aided Wireless Communications: Adaptive Beamforming and Experimental Validations

Abstract: Recently, the fifth generation of cellular mobile communications (5G) network has been deployed and become pervasive. 5G offers a significant increase in terms of bandwidth and data rate compared to the previous generations. In addition, new technologies such as millimeter-wave (mmWave) technology and massive MIMO (mMIMO), have been proposed to meet the demand. However, some inevitable challenges still exist. In mmWave frequency, path loss and shadowing become more severe due to the radio electromagnetic (EM) wave characteristics. In this paper, we propose the utilization of reconfigurable intelligent surface (RIS) to aid wireless communications to overcome path loss and shadowing issues, by using a compressive sensing-based adaptive beamforming algorithm. To validate the theory, hypothesis, and simulation results, we have designed, fabricated, and conducted experiments with a 1-bit RIS testbed. The results show that the bit error rate (BER) and signal-to-noise ratio (SNR) of the received signal are significantly improved when the proposed RIS is employed. Further, we have also demonstrated a video streaming application aided by the proposed RIS as one of the potential RIS deployment scenarios.

Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

Abstract: This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel model. In particular, for a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs a sequence of adaptive sensing vectors based on the available information so far at the base station (BS). By recognizing that the posterior distribution of the AoA provides sufficient statistic for solving the initial access problem, we consider the AoA posterior distribution as the main component of the input to the proposed DNN for designing the adaptive beamforming strategy. However, computing the AoA posterior distribution can be computationally challenging when the fading coefficient is unknown. To address this issue, this paper proposes to use the minimum mean squared error (MMSE) estimate of the fading coefficient to compute an approximation of the posterior distribution. Numerical results demonstrate that as compared to the existing adaptive beamforming schemes utilizing predesigned hierarchical codebooks, the proposed deep learning-based adaptive beamforming achieves a higher AoA detection performance.

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network

Abstract: Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.

An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

Abstract: To develop an adaptive beamformer against the signal of interest (SOI) steering vector mismatch, a robust Capon beamformer (RCB) like steering vector estimation method based on the interference matrix reduction is proposed. Different from the RCB and its modified versions that optimize the SOI steering vector with the Capon power estimator, this article designs an SOI power estimator to formulate the steering vector optimization problem with an uncertainty set constraint. In terms of that, the unknown SOI covariance matrix is needed to realize the SOI power estimator, an efficient interference matrix reconstruction way via SOI blocking and matrix eigen-transition is exploited to reduce the interference component from the sample covariance matrix. Herein, after solving the given steering vector optimization problem and adding the noise component to the aforesaid interference matrix, the weight vector of the derived algorithm is, thereby, computed using the estimated SOI steering vector and interference covariance matrix. The proposed method only requires the source number and prior direction of the SOI. The numerical simulations show that the proposed approach can outperform the compared ones with reduced complexity in the situation of various steering vector mismatches.

Erratum to “Lamb Waves and Adaptive Beamforming for Aberration Correction in Medical Ultrasound Imaging”

Abstract: Phase aberration in transcranial ultrasound imaging (TUI) caused by the human skull leads to an inaccurate image reconstruction. In this article, we present a novel method for estimating the speed of sound and an adaptive beamforming technique for phase aberration correction in a flat polyvinylchloride (PVC) slab as a model for the human skull. First, the speed of sound of the PVC slab is found by extracting the overlapping quasi-longitudinal wave velocities of symmetrical Lamb waves in the frequency–wavenumber domain. Then, the thickness of the plate is determined by the echoes from its front and back side. Next, an adaptive beamforming method is developed, utilizing the measured sound speed map of the imaging medium. Finally, to minimize reverberation artifacts caused by strong scatterers (i.e., needles), a dual probe setup is proposed. In this setup, we image the medium from two opposite directions, and the final image can be the minimum intensity projection of the inherently co-registered images of the opposed probes. Our results confirm that the Lamb wave method estimates the longitudinal speed of the slab with an error of 3.5% and is independent of its shear wave speed. Benefiting from the acquired sound speed map, our adaptive beamformer reduces (in real time) a mislocation error of 3.1, caused by an 8 mm slab, to 0.1 mm. Finally, the dual probe configuration shows 7 dB improvement in removing reverberation artifacts of the needle, at the cost of only 2.4-dB contrast loss. The proposed image formation method can be used, e.g., to monitor deep brain stimulation procedures and localization of the electrode(s) deep inside the brain from two temporal bones on the sides of the human skull.

Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation

Abstract: This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

Minimum Variance Combined With Modified Delay Multiply-and-Sum Beamforming for Plane-Wave Compounding

Abstract: Plane-wave compounding is an active topic of research in ultrasound imaging because it is a promising technique for ultrafast ultrasound imaging. Unfortunately, due to the data-independent nature of the traditional compounding method, it imposes a fundamental limit on image quality. To address this issue, adaptive beamformers have been implemented in the compounding procedure. In this article, a new adaptive beamformer for the 2-D data set obtained from multiple plane-wave transmissions is investigated. In the proposed scheme, the minimum variance (MV) weights are applied to the backscattered echoes. Then, the final image is obtained by employing a modified version of the delay multiply-and-sum (DMAS) beamformer in the coherent compounding. The results demonstrate that the presented MV-DMAS scheme outperforms the conventional coherent compounding in both terms of resolution and contrast. It also offers improvements over the 2-D-DMAS and some MV-based methods presented in the literature, such that it achieves at least 20.9% enhancement in sidelobe reduction compared with the best result of MV-based methods. Also, by the proposed method, the in vivo study shows an improved generalized contrast-to-noise ratio (GCNR) that implies a higher probability of lesion detection.

Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction With Annular Uncertainty Set and Vector Space Projection

Abstract: The performance of adaptive beamforming will degrade dramatically in practical application due to system errors including signal direction error, array geometry error, gain, and phase errors. Besides, the performance will further deteriorate when the desired signal is contained in the sample data. Therefore, a robust adaptive beamforming method based on the covariance matrix reconstruction with annular uncertainty set (AUS) and vector space projection (VSP) is proposed in this letter. By integrating the corresponding Capon spectrum over the surface of AUS, the interference-plus-noise covariance matrix (INCM) and the desired signal covariance matrix are reconstructed, respectively. Because the steering vector (SV) of desired signal lies in the intersection of the signal subspaces of the sample covariance matrix and the reconstructed desired signal covariance matrix, it can be estimated by the VSP method. Finally, the adaptive weight vector is calculated based on the reconstructed INCM and the estimated SV. Simulation and experiment results show that the proposed method can effectively suppress the interference and achieve excellent performance under system errors.

Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

Abstract: This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel. For a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance.

Ship Echo Identification Based on Norm-Constrained Adaptive Beamforming for an Arrayed High-Frequency Coastal Radar

Abstract: A frequency-modulated continuous-wave (FMCW) radar, operated at the central frequency of 27.75 MHz and the bandwidth of 300 kHz has been established on the seashore near the Taichung harbor ( $24^{\circ } 18.591^\prime $ N, $120^{\circ } 31.389^\prime $ E), Taiwan. Sixteen vertical dipole antennas were located linearly and attached with 16 receiving channels. One purpose of the radar is to monitor the ships that navigate toward, away, and around the harbor. In this article, we applied the radar beamforming methods that transform the temporal radar signals as brightness on the 2-D range-azimuthal domain, making the ship echoes visible directly on the spatial domain. Three beamformers, linear Fourier, directionally constrained minimum power (DCMP), and norm-constrained DCMP (NC-DCMP) algorithms, were employed to produce range–angle (RA) brightness distribution that is different from the conventionally used range–Doppler (RD) spectra in ship detection. Both DCMP and NC-DCMP are adaptive beamforming methods. With the auxiliary of a band-stop filter to suppress the sea echoes, the NC-DCMP beamformer was demonstrated to surpass the other two beamformers and could provide more visible ship echoes in the RA brightness distribution. Automatic Identification System (AIS) information was also used to validate the radar-determined ship locations from the RA brightness distribution. Although some ships having the AIS information were not observed clearly by the radar, the radar detected some targets without AIS information.

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

Abstract: Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and 10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

Adaptive Beamforming Design of Planar Arrays Based on Bayesian Compressive Sensing

Abstract: An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded. Moreover, while the linear array has been actively researched, few studies have focused on the planar array. In this paper, an adaptive beamformer with more accurate reconstruction of the covariance matrix for a planar array is therefore proposed. The reconstruction is based on the Bayesian compressive sensing (BCS) theory. First, the directions of arrival (DOA) estimation of interferences are conducted. This problem is transformed into that of finding the minimum number of DOAs with a nonzero input because the array output is known. Accordingly, it can be converted into a probabilistic framework using the BCS technique. Then, the interference plus noise covariance matrix is reconstructed by using the DOAs of the interferences and the Capon spatial spectrum estimator. The reconstruction matrix is more accurate than other methods that directly use a data sampling matrix. Further constraints are then added to control the side-lobe level of the beam pattern of the proposed beamformer. Our numerical results confirm the effectiveness of the proposed method in terms of interference suppression, robustness to mismatch errors, and effective side-lobe-level control.

User Parameter-Free Minimum Variance Beamformer in Medical Ultrasound Imaging

Abstract: The minimum variance beamformer (MVB) is a well-known adaptive beamformer in medical ultrasound imaging. Accurate estimation of the covariance matrix has a great effect on the performance of the MVB. In adaptive ultrasound imaging, parameters such as the subarray length, the number of samples used for temporal averaging, and the value of diagonal loading (DL) have the main role in the true estimation of the covariance matrix. The optimal values for these parameters are different from one scenario to another one. Thus, the MVB is not a parameter-free method, and its behavior is scenario-dependent. In the field of telecommunications and radar, the shrinkage method was proposed to determine the DL factor, but no method has been provided yet to determine other parameters. In this article, an adaptive approach is developed to determine the MVB parameters, which is completely independent of the user. The minimum variance variable loading along with the modified shrinkage (MVVL-MSh) algorithm is introduced to adaptively calculate the optimal DL. Also, two methods based on the coherence factor (CF) are proposed to determine the subarray length in the spatial smoothing and the number of samples required for temporal averaging. The performance of the proposed methods is evaluated using simulated and experimental RF data. It is shown that the methods preserve the contrast and improve the resolution by about 35% and 38% compared to the MV having a fix loading coefficient and the MV-Sh algorithm.

Spatiotemporal Coherence Weighting for In Vivo Cardiac Photoacoustic Image Beamformation

Abstract: Photoacoustic (PA) image reconstruction generally utilizes delay-and-sum (DAS) beamforming of received acoustic waves from tissue irradiated with optical illumination. However, nonadaptive DAS reconstructed cardiac PA images exhibit temporally varying noise which causes reduced myocardial PA signal specificity, making image interpretation difficult. Adaptive beamforming algorithms such as minimum variance (MV) with coherence factor (CF) weighting have been previously reported to improve the DAS image quality. In this article, we report on an adaptive beamforming algorithm by extending CF weighting to the temporal domain for preclinical cardiac PA imaging (PAI). The proposed spatiotemporal coherence factor (STCF) considers multiple temporally adjacent image acquisition events during beamforming and cancels out signals with low spatial coherence and temporal coherence, resulting in higher background noise cancellation while preserving the main features of interest (myocardial wall) in the resultant PA images. STCF has been validated using the numerical simulations and in vivo ECG and respiratory-signal-gated cardiac PAI in healthy murine hearts. The numerical simulation results demonstrate that STCF weighting outperforms DAS and MV beamforming with and without CF weighting under different levels of inherent contrast, acoustic attenuation, optical scattering, and signal-to-noise (SNR) of channel data. Performance improvement is attributed to higher sidelobe reduction (at least 5 dB) and SNR improvement (at least 10 dB). Improved myocardial signal specificity and higher signal rejection in the left ventricular chamber and acoustic gel region are observed with STCF in cardiac PAI.

Simultaneous Interference Localization and Array Calibration for Robust Adaptive Beamforming With Partly Calibrated Arrays

Abstract: In this article, we address the problem of robust adaptive beamforming in the presence of array sensor miscalibration. We consider the use of partly calibrated linear arrays, where only a small portion of sensors have been gain-phase aligned. Our solution is based on the interference-plus-noise covariance matrix (INCM) reconstruction principle. In our solution, the INCM is reconstructed by performing simultaneous interference localization and array calibration (SILAC). Toward this end, a novel virtual baseline extension technique is presented for high-accuracy SILAC. After SILAC, the interference and noise powers are estimated, and the INCM is reconstructed subsequently. No computations of integration/summation and nonlinear optimization are involved in our beamformer, which is termed as “INCM-SILAC” beamformer. Numerical examples are offered to validate the performance of the INCM-SILAC beamformer. A MATLAB code for reproducing the results of radar application example is available at https://github.com/jinhesjtu/SILAC.git

Flexible Robust Adaptive Beamforming Method With Null Widening

Abstract: The nonstationarity of interferences and array errors may bring fatal degradation to the interference cancellation performance of an adaptive beamformer. Covariance matrix tapering (CMT) can produce wide troughs in receiving pattern and becomes a promising solution. However, not only the full adaption methods but also the robust sidelobe canceller widen all nulls symmetrically to the original directions of arrival (DOAs) with the same width. In fact, in most cases, the adaptive pattern does not need symmetrical and equivalent-width nulls since it is of little possibility that different interference poses the same nonstationarity. To cover the worst nonstationarity, traditional CMT methods need to produce a widest null and will suffer a waste of degrees of freedom (DOFs). In this paper, a flexible method for null widening is proposed which can produce wide null with different desired width and asymmetry. The asymmetry is developed from the spatial asymmetrical interference cluster, and the unequal null width is produced by disturbing different interference space with different tapering matrix. Computer simulation result corroborate the feasibility and merits of the proposed method.

Delay-and-sum-to-delay-standard-deviation factor: a promising adaptive beamformer

Abstract: A new adaptive weighting method [delay-and-sum-to-delay-standard-deviation factor (DASDSF)] combined with minimum variance (MV) beamforming is introduced in photoacoustic imaging (PAI). Existing MV-based beamformers improve photoacoustic image quality in terms of achieving narrow main lobes and, thus, improving spatial resolution. But, the beamformers give a strong side-lobe signal strength that greatly degrades the reconstructed image contrast. As a feedback weighting factor, DASDSF addresses the persisting side-lobe issue present in MV-beamformed images, i.e., our proposed method is robust against reduction in noises as well as side lobes, and it outperforms MV and MV combined with coherence factor beamformers. Validation studies-being carried out both in numerical simulation and experiments employing a low-cost (16 elements) linear transducer array in a home-built PAI system-demonstrate an excellent performance of the proposed weighting approach in improving SNR, while reducing main-lobe width (i.e., FWHM) and side-lobe signal strength. The study demonstrates that the proposed algorithm holds promise for development of a cost-effective PAI system using a low-cost linear transducer (∼16 elements against ∼128 generally used).

Noise Adaptive Beamforming for Linear Array Photoacoustic Imaging

Abstract: The delay-and-sum (DAS) algorithm is widely used for beamforming in linear array photoacoustic (PA) imaging systems and is characterized by fast execution. However, these algorithms suffer from various drawbacks, such as low resolution, low contrast, high sidelobe artifacts, and lack of visual coherence. More recently, adaptive weighting was introduced to improve the reconstruction image quality. Unfortunately, the existing state-of-the-art adaptive beamforming algorithms are computationally expensive and do not consider the specific noise characteristics of the acquired ultrasonic signal. In this article, we present a new adaptive weighting factor named the variational coherence factor (VCF), which takes into account the noise level variations of radio frequency data. The proposed technique provides superior results in terms of image resolution, sidelobe reduction, signal-to-noise ratio (SNR), and contrast level improvement. The quantitative results of the microsphere phantom imaging show that the proposed VCF-assisted DAS method leads to 55% and 25% improvements in full-width-at-half-maximum (FWHM) and 57% and 32% improvement in SNR, respectively, compared to the state-of-the-art DAS-based methods. The results demonstrate that the proposed method can effectively improve the reconstructed image quality and deliver satisfactory imaging performance even with a limited number of sensor elements. The proposed method can potentially reduce the instrumentation cost of the PA imaging system and contribute toward the clinical translation of the modality.

Adaptive Beamforming via Desired Signal Robust Removal for Interference-Plus-Noise Covariance Matrix Reconstruction

Abstract: To tackle the problem of the desired signal (DS) steering vector mismatch, especially in the situation of direction-of-arrival error and array perturbations, a robust interference-plus-noise covariance matrix (INCM) reconstruction method based upon DS removal is presented Unlike previous studies, this paper proposes to remove the DS component from the training data by building a blocking matrix, which is computed as the inverse of the DS-plus-noise covariance matrix (DSNCM) More specifically, to increase the robustness against arbitrary mismatches, the DS steering vector estimated as the prime eigenvector of the DS matrix, which is attained through integrating the Capon spectrum estimator over the annulus uncertainty sets of the mainlobe region in advance, is adopted to give a faithful blocking matrix After that, utilizing the obtained blocking matrix to process the training data, the quasi INCM is computed indeed Finally, a precise INCM is reconstructed by projecting the principal components of the quasi INCM onto the aforesaid DSNCM Numerical simulations have illustrated that the proposed adaptive beamformer can outperform the existing ones and gain almost optimal performance under different scenarios

Sparse Array Beamforming Design for Coherently Distributed Sources

Abstract: Recently, nonstructured sparse array designs have attracted wide interest due to their capability of providing optimum performance for environment-dependent adaptive beamforming. In this article, we develop a sparse array design approach for adaptive beamforming in the presence of spatially coherently distributed (CD) sources. The proposed approach formulates the design problem via maximizing the output signal-to-interference-plus-noise ratio (SINR), but it exploits the generalized array manifold of CD sources. Moreover, sequential convex programming is utilized to convert the nonconvex optimization problem into a series of convex subproblems. We also analyze the impact of source distributed shape on optimum array configuration and theoretically prove that when the CD sources are Gaussian-shaped, the uniform linear array (ULA) is exactly the optimum array configuration. The resulting optimum sparse arrays yield higher output SINR and better-shaped beampattern than the structured sparse arrays. Numerical results verify the superiority of the optimum sparse arrays obtained by the proposed approach over the structured sparse arrays.

Robust Minimum Variance Beamforming With Sidelobe-Level Control Using the Alternating Direction Method of Multipliers

Abstract: Adaptive beamforming with sidelobe-level control in the presence of signal steering vector uncertainty is investigated. Unlike the traditional multiconstrained optimization strategy using the interior point method, iterative optimization algorithms with the aid of the alternating direction method of multipliers (ADMM) framework are proposed. The uncertainty set constraint and the sidelobe constraint are formulated into two optimization subproblems and handled with the Lagrange multiplier method. By introducing matrix decomposition techniques, subproblem 1 is transformed into a polynomial root-finding problem that can be solved with low computational complexity. For subproblem 2, a closed-form solution can be obtained directly. Furthermore, for the continuously receiving snapshots case, iterative gradient minimization is introduced and embedded into the ADMM iterations to give an approximate solution free from matrix decompositions. Theoretical analyses and simulations verify the low complexities and performance advantages of the proposed algorithms in the low sample support, steering vector mismatch, and real-time snapshot update scenarios.

Robust Adaptive Beamforming via Covariance Matrix Reconstruction and Interference Power Estimation

Abstract: The performance of the traditional Capon beamformer degrades sharply when the signal of interest (SOI) appears in the training data. To reduce the impact of SOI on the Capon beamformer, two methods for the interference-plus-noise covariance matrix (INCM) reconstruction are proposed in this letter. The proposed-1 method is based on the integral of the Capon spectrum without the residual noise power. In the proposed-2 method, the interference power is estimated via the orthogonality between different sparse steering vectors (SVs) to project the sample covariance matrix for the INCM reconstruction. Meanwhile, the inverse of INCM is obtained by eigenvalue decomposition and the SV of SOI is updated by the principal eigenvector of the reconstructed SOI covariance matrix (SCM). Simulation results show that the proposed methods are robust against some mismatch errors.