Showing papers on "Adaptive beamformer published in 2022"

Robust adaptive beamforming based on covariance matrix reconstruction with RCB principle

Abstract: In this paper, a novel robust adaptive beamforming (RAB) technique based on the covariance matrix reconstruction is proposed to solve the array model mismatch. The proposed technique provides an alternative method to reconstruct the interference-plus-noise covariance matrix (IPNCM) and estimate the steering vector (SV) of the desired signal. In particular, an unknown error exists in the SV of the considered array. The proposed RAB algorithm adopts the robust Capon beamformer (RCB) principle to roughly estimate the SVs of the desired and interference signals. Based on those preliminary estimated SVs, an improved Capon power spectrum is constructed. Then the interference covariance matrix is reconstructed by utilizing the modified Capon power spectrum integrated over the union of several disjoint angular sectors. Then, the reconstructed covariance matrix is further refined by exploring the low rank property. Meanwhile, the SV of the desired signal is estimated by solving a modified quadratically constrained quadratic programming (QCQP) problem. The simulation results show that the RCB principle provides an accurate IPNCM reconstruction, and the proposed RAB algorithm outperforms the existing RAB techniques over a wide range of input signal-to-noise ratio (SNR) region under various mismatch conditions.

Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement

Abstract: A signal processing approach combining beamforming with mask-informed speech enhancement was assessed by measuring sentence recognition in listeners with mild-to-moderate hearing impairment in adverse listening conditions that simulated the output of behind-the-ear hearing aids in a noisy classroom. Two types of beamforming were compared: binaural, with the two microphones of each aid treated as a single array, and bilateral, where independent left and right beamformers were derived. Binaural beamforming produces a narrower beam, maximising improvement in signal-to-noise ratio (SNR), but eliminates the spatial diversity that is preserved in bilateral beamforming. Each beamformer type was optimised for the true target position and implemented with and without additional speech enhancement in which spectral features extracted from the beamformer output were passed to a deep neural network trained to identify time-frequency regions dominated by target speech. Additional conditions comprising binaural beamforming combined with speech enhancement implemented using Wiener filtering or modulation-domain Kalman filtering were tested in normally-hearing (NH) listeners. Both beamformer types gave substantial improvements relative to no processing, with significantly greater benefit for binaural beamforming. Performance with additional mask-informed enhancement was poorer than with beamforming alone, for both beamformer types and both listener groups. In NH listeners the addition of mask-informed enhancement produced significantly poorer performance than both other forms of enhancement, neither of which differed from the beamformer alone. In summary, the additional improvement in SNR provided by binaural beamforming appeared to outweigh loss of spatial information, while speech understanding was not further improved by the mask-informed enhancement method implemented here.

Robust Adaptive Beamforming Based on Subspace Decomposition, Steering Vector Estimation and Correction

Abstract: Considering that the performance of adaptive arrays is sensitive to any type of mismatches, an innovative robust adaptive beamforming method based on covariance matrix reconstruction, subspace decomposition, steering vector estimation and correction is proposed. Based on Capon spatial spectrum, a group of angle sets containing all interfering signals are determined, and the interference covariance matrix can be reconstructed with a smaller integration interval. On the other hand, the sample covariance matrix can be decomposed into signal subspace and interference-plus-noise by using the principle of maximum correlation. Based on the interference-plus-noise subspace and the reconstructed signal-plus-noise covariance matrix, a new convex optimization model is built to estimate the steering vector of the desired signal. Then, an improved projection approach based on signal subspace is designed for correction to improve the robustness against the nominal direction vector mismatches. Simulation results demonstrate that the proposed method achieves better overall performance under multiple mismatches over a wide range of input signal-to-noise ratios.

Switchable and Tunable Deep Beamformer Using Adaptive Instance Normalization for Medical Ultrasound

Abstract: Recent proposals of deep learning-based beamformers for ultrasound imaging (US) have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be readily combined. Unfortunately, with the existing technology, a large number of beamformers need to be trained and stored for different probes, organs, depth ranges, operating frequency, and desired target 'styles', demanding significant resources such as training data, etc. To address this problem, here we propose a switchable and tunable deep beamformer that can switch between various types of outputs such as DAS, MVBF, DMAS, GCF, etc., and also adjust noise removal levels at the inference phase, by using a simple switch or tunable nozzle. This novel mechanism is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated using a single generator by merely changing the AdaIN codes. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed method for various applications.

Null-Steering Beamformers for Suppressing Unknown Direction Interferences in Sidelobes

Abstract: This paper proposes an efficient null-steering beamformer for adaptive pattern synthesis of uniform linear arrays based on binary bat algorithm (BBA) and the amplitude-only control of array excitation weights. The proposed beamformer is able to suppress unknown direction interferences in the sidelobes while simultaneously maintaining the main lobe and suppressing the sidelobes. The performance of the proposal has been investigated via a couple of scenarios including operation speed and adaptive null-steering ability with or without the effect of mutual coupling in half-wave dipole uniform linear arrays (DULA). The simulation results have proved that the proposed beamformer is a promising approach for adaptive pattern synthesis in the case of interference suppression. In addition, this proposal outperforms those using binary particle swarm optimization (BPSO) on the operation speed and null-steering efficiency.

A Robust Digital Beamforming on Receive in Elevation for Airborne MIMO SAR System

Abstract: The echo separation issue for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is usually regarded as a more technical challenge. Spotlighted as a promising solution to the echo separation, the well-known short-term shift-orthogonal (STSO) beamforming scheme has become increasingly popular. However, for airborne MIMO SAR systems, the digital beamforming (DBF) involved in the STSO scheme usually encounters more issues, e.g., the direction of arrival (DOA) mismatch induced by topography variation. Up to now, relatively less research on robust DBF processing has been conducted for airborne MIMO SAR systems. In this respect, an adaptive DBF technique, based on interference plus noise covariance matrix (IPNCM) reconstruction and desired signal steering vector estimation, has been proposed in this paper. IPNCM reconstruction and steering vector estimation can not only cope with the DOA mismatch problem, but also remove the desired signal component in the training data cells to increase the beamformer convergence rates. Consequently, the proposed approach really improves the array output signal-to-interference-plus-noise ratio (SINR). Moreover, numerous discussions and simulations are carried out to prove the effectiveness of proposed DBF technique under various disturbance environments. Compared with the current DBF techniques, the proposed method provides a bright application prospect for the STSO scheme.

New Compact Antenna Array for MIMO Internet of Things Applications

Abstract: A communication system is proposed for the Internet of Things (IoT) applications in desert areas with extended coverage of regional area network requirements. The system implements a developed six-element array that operates at a 2.45 GHz frequency band and is optimized to reduce the size and limit element coupling to less than −20 dB. Analysis of the proposed system involves a multiple-input multiple-output (MIMO) operation to obtain the diversity gain and spectral efficiency. In addition, the radiation efficiency of the proposed antenna is greater than 65% in the operation bandwidth (more than 30 MHz) with a peak of 73% at 2.45 GHz. Moreover, an adaptive beamforming system is presented based on monitoring the direction of arrival (DOA) of various signals using the root MUSIC algorithm and utilizing the DOA data in a minimum variance distortionless response (MVDR) technique beamformer. The developed array is found to have an envelope correlation coefficient (ECC) value of less than 0.013, mean effective gain (MEG) of more than 1 dB, diversity gain of more than 9.9 dB, and channel capacity loss (CCL) of less than 0.4 bits/s/Hz over the operation bandwidth. Adaptive beamforming is used to suppress interference and enhance the signal-to-interference noise ratio (SINR) and is found to achieve a data rate of more than 50 kbps for a coverage distance of up to 100 km with limited power signals.

A New Adaptive Beamforming of Multiband Fractal Antenna Array in Strong-Jamming Environment

Abstract: Abstract This paper proposes, for the first time, a new radiation pattern synthesis for fractal antenna array that combines the unique multi-band characteristics of fractal arrays with the adaptive beamforming requirements in wireless environment with high-jamming power. In this work, a new adaptive beamforming method based on discrete cbKalman filter is proposed for linear Cantor fractal array with high performance and low computational requirements. The proposed Kalman filter-based beamformer is compared with the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) techniques under various parameter regimes, and the results reveal the superior performance of the proposed approach in terms of beamforming stability, Half-Power Beam Width (HPBW), maximum Side-Lobe Level (SLL), null depth at the direction of interference signals, and convergence rate for different Signal to Interference (SIR) values. Also, the results demonstrate that the suggested approach not only achieves perfect adaptation of the radiation pattern synthesis at high jamming power, but also keep the same SLL at different operating frequencies. This shows the usefulness of the proposed approach in multi-band smart antenna technology for mobile communications and other wireless systems.

A Novel Realistic Approach of Adaptive Beamforming Based on Deep Neural Networks

Abstract: A new deep neural network (NN) approach applied to antenna array adaptive beamforming is presented in this article. A recurrent NN (RNN) based on the gated recurrent unit (GRU) architecture is used as a beamformer in order to produce proper complex weights for the feeding of the antenna array. The proposed RNN utilizes four hidden GRU layers and one extra layer for linear transformation. The produced weights are subsequently compared with respective weights derived by a null steering beamforming (NSB) technique in order to measure the accuracy of the RNN. The RNN training is performed by using a large dataset derived from an NSB technique applied to a realistic microstrip linear antenna array, in order to consider real-world effects, such as the nonisotropic radiation pattern of an array element and the mutual coupling between the array elements. The RNN performance is examined by using the root-mean-square error metric, whereas its beamforming performance is evaluated by estimating the mean value and the standard deviation of the divergences of the main lobe and nulls directions from their respective desired directions. A comparison between various NN structures and an overall study of the proposed RNN-based beamformer are also presented.

Adaptive Beamforming Approaches to Improve Passive Radar Performance in Sea and Wind Farms’ Clutter

Abstract: This article presents the problem of passive radar vessel detection in a real coastal scenario in the presence of sea and wind farms’ clutter, which are characterised by high spatial and time variability due to the influence of weather conditions. Deterministic and adaptive beamforming techniques are proposed and evaluated using real data. Key points such as interference localisation and characterisation are tackled in the passive bistatic scenario with omnidirectional illuminators that critically increase the area of potential clutter sources to areas far from the surveillance area. Adaptive beamforming approaches provide significant Signal-to-Interference improvements and important radar coverage improvements. In the presented case study, an aerial target is detected 28 km far from the passive radar receiver, fulfilling highly demanding performance requirements.

Fundamental Approaches to Robust Differential Beamforming With High Directivity Factors

Abstract: Differential beamforming, which measures the spatial derivatives of the acoustic pressure field, can be used in a wide range of small devices that require high-fidelity sound and speech acquisition as it can achieve frequency-invariant spatial responses with high directivity factors (DFs). Since a differential process is inherently sensitive to sensors' self noise and other array imperfections, the most challenging problem in the design of any differential beamformer is how to achieve the maximum possible DF while maintaining a proper level of robustness for practical usage. While significant efforts have been made on this topic, the problem remains unsolved and further study is indispensable. This paper is devoted to dealing with this challenging problem. It presents a study on theory and methods to achieve the optimal and fundamental compromise between the white noise gain (WNG), which quantifies how robust is the beamformer, and the DF in differential beamforming. The major contributions of this work are as follows. 1) We show and prove that any null constrained fixed beamformer can be decomposed as the sum of two orthogonal filters, i.e., the maximum WNG (MWNG) beamformer and a reduced-rank one. Based on this decomposition, we develop three kinds of differential beamformers from the WNG perspective, which can achieve a flexible and optimal compromise between DF and WNG. 2) We show that a transformed null constrained beamformer can also be decomposed as the sum of two orthogonal filters, i.e., the transformed maximum DF (MDF) beamformer and another reduced-rank one. Based on this decomposition, we also develop three kinds of differential beamformers, which can obtain the desired level of DF while using the rest of the degrees of freedom to maximize the WNG. Simulations are performed to validate the theoretical analysis and developed differential beamformers.

A Compact Noise Covariance Matrix Model for MVDR Beamforming

Abstract: Acoustic beamforming is routinely used to improve the SNR of the received signal in applications such as hearing aids, robot audition, augmented reality, teleconferencing, source localisation and source tracking. The beamformer can be made adaptive by using an estimate of the time-varying noise covariance matrix in the spectral domain to determine an optimised beam pattern in each frequency bin that is specific to the acoustic environment and that can respond to temporal changes in it. However, robust estimation of the noise covariance matrix remains a challenging task especially in non-stationary acoustic environments. This paper presents a compact model of the signal covariance matrix that is defined by a small number of parameters whose values can be reliably estimated. The model leads to a robust estimate of the noise covariance matrix which can, in turn, be used to construct a beamformer. The performance of beamformers designed using this approach is evaluated for a spherical microphone array under a range of conditions using both simulated and measured room impulse responses. The proposed approach demonstrates consistent gains in intelligibility and perceptual quality metrics compared to the static and adaptive beamformers used as baselines.

A data alternating extraction general structure and its algorithms for adaptive space-time wideband beamforming

Abstract: The adaptive space-time processing is a well-substantiated method to build a wideband beamformer. This paper proposes a low complexity general structure for the adaptive space-time wideband beamforming. The new general structure uses a data alternating extraction approach for adjacent arrays to reduce the scales of received data, resulting in obtaining the optimum adaptive weights with much lower computational complexity. Then, the adaptive wideband beamforming algorithms are proposed for the general structure. Among the novel algorithms, the singular value decomposition alternating extraction frequency constrained Frost beamformer (SVD-AEFCFB) and SVD-AEFC Laguerre beamformer (SVD-AEFCLB) both have an undistorted response to the wideband signal of interest and satisfactory anti-interference capability with the lowest computational complexity. Importantly, the proposed structure and algorithms have the same generalized and popularized characteristics for the other space-time methods. Simulation results highlight the correctness and effectiveness of the proposed methods.

Massive Multiple-Input Multiple-Output Antenna Architecture for Multiband 5G Adaptive Beamforming Applications

Abstract: One major challenge to full 5G systems deployment especially in mm-Wave band is the poor signal propagation. One approach to mitigate this effect is the use of new 5G technologies such as massive MIMO, adaptive beamforming, reconfigurable antennas etc. which can enhance the performance of the system. Adaptive beamforming algorithm uses advance digital signal processing techniques to generate main beams in the direction of interest while placing nulls in interfering signals direction to reduce interference. The beams are formed in the receiver rather in free space. It is therefore very crucial to develop an algorithm that can optimize the system to improve performance by generating signals at a faster convergence rate.In this paper, the performance analysis of various adaptive beamforming systems for 5G applications are presented using various LMS algorithms including a novel sign-leaky LMS algorithm. A uniform linear array antenna of varying element configurations, inter-element spacing, varying step-size, direction of arrival angles of the desired signals are analysed using various algorithms to determine the optimum performance of the systems. Simulation result shows that the convergence rate is highly enhanced, with the proposed algorithm converging with at least 5 iterations less than conventional LMS algorithm, while reducing interference effects by placing deeper nulls in interfering signal direction of arrivals using the proposed beamforming algorithm. There is also at least-2dB drop in normalized power of the sidelobe level compared to the LMS algorithm.

Robust Adaptive Transmit Beamforming under the Constraint of Low Peak-to-Average Ratio

Abstract: In radar detection, in order to make the beam have variable directivity, a Capon beamformer is usually used. Although this traditional beamformer enjoys both high resolution and good interference suppression, it usually leads to high sidelobe and is sensitive to array steering vector (ASV) mismatch. To overcome such problems, this study devises a novel, robust adaptive beamformer that is robust to ASV mismatch under the constraint where the sidelobe is oriented to the ground. Moreover, to make full use of the transmit power, the constraint of a low peak-to-average power ratio (PAPR) is also taken into consideration. Accordingly, this robust adaptive beamformer is developed by optimizing a transmitting beamformer constrained by ASV mismatch and low PAPR. This optimization problem is transformed into a second-order cone programming (SOCP) problem which can be efficiently and exactly solved. The proposed transmit beamformer possesses not only adaptive interference rejection ability and robustness against ASV mismatch, but also direct sidelobe control and a low PAPR. Simulation results are presented to demonstrate the superiority of the proposed approach. The proposed method can make the peak sidelobe level (PSL) level on the ground side below −30 dB.

Sparse Array Beamformer Design via ADMM

Abstract: In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method uses the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. Numerical results exhibit excellent performance of the proposed method, which is comparable to that of the exhaustive search approach.

Investigation on improving performance of adaptive beamformer by statistical analysis of ultrasonic echoes

Abstract: Minimum variance (MV) beamformers have been introduced in medical ultrasound imaging to improve image quality. In most cases, the MV beamformers have been investigated in terms of resolution improvement. However, the contrast-to-noise ratio (CNR) is also a clinically important metrics and gathers attention recently. In this study, we examined the diagonal loading parameter σ in MV beamforming and determined its appropriate value by evaluating image quality evaluation metrics including CNR. In order to further improve the image quality, a method for determining the value of σ based on the difference in statistical properties of received ultrasonic echo signals was also investigated. The phantom experimental results showed that the proposed method achieved a better CNR than the conventional MV beamformer while keeping resolution significantly better than that in delay-and-sum beamforming.

Robust Beamforming Based on Complex-Valued Convolutional Neural Networks for Sensor Arrays

Abstract: Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This paper proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer.

A Fast Adaptive Beamforming Technique for Efficient Direction-of-Arrival Estimation

Abstract: A plethora of problems related to signal estimation and direction can be effectively solved by using a sensor array. To this end, uniform estimation for all direction-of-arrivals (DOAs) with low computational complexity is an indispensable step for numerous applications. In this article, we present the DOA estimation method exploiting the recently proposed fast variable step size least mean square (FVSSLMS) method (Jalal et al., 2020). In most of the DOA estimation works, a uniform linear array (ULA) was used. Nonetheless, a ULA does not perform well for the DOA estimation when the signal sources are impinging close to the array endfire. To circumvent this issue, we modify the structure of ULA by displacing its four sensors to the top and bottom of the first and the last sensor. Computer simulations demonstrate that the proposed method provides uniform estimation performance for all DOAs with reduced computation complexity.

Septennial Adaptive Beamforming Algorithm

Abstract: With rapid advancement in technology, human kind has been able to come through wired landlines to handheld smart phone devices in just a few years of time. This is possible because of the digital signal processing (DSP). In wireless communications, with multiple access methods and high performing algorithms which DSP module makes use of, the machinery at the base station gets boosted up. This increases capacity of the RF spectrum which is well known to be a limited resource. The Quality of Service (QoS) also gets better with lesser interference and mean square error. The paper presents one such algorithm for the DSP block of the smart antenna system. The proposed algorithm called Septennial Adaptive Beamforming Algorithm (SABA), having seven functioning modules or blocks in it, reduces the interference and lowers the mean square error with increase in converging speed at lesser number of iterations.

Robust adaptive null broadening beamforming based on subspace projection

Abstract: When the rapid moving interference exists, especially in the presence of look direction mismatch, coherent local scattering and so on, the output performance of beamformer will degrade dramatically. To solve this problem, a novel robust adaptive null broadening beamforming method based on subspace projection is proposed. First, the proposed beamformer requires a priori information, that is, the spatial sector where the desired signal and interference may locate, so as to construct the signal-plus-noise covariance matrix (SPNCM) and interference-plus-noise covariance matrix (IPNCM). Then, the desired signal steering vector (SV) is calibrated twice. The desired signal SV is estimated from the SPNCM and subsequently calibrated by using the uncertainty set optimisation method. Next, we construct the null broadening projection matrix by tapering the interference subspace got from IPNCM. Finally, the sample covariance matrix is processed by projection technology and diagonal loading technology. The optimal weight vector is obtained by the new covariance matrix and calibrated desired signal SV. Simulation results show that the proposed method can form wide null in the incident direction of interference and prevent interference from moving out of the null when the rapid moving interference exists. The proposed method outperforms other existing methods.

Robust adaptive beamforming algorithm based on damped singular value decomposition regularization

Abstract: When adaptive beamformers are applied to actual problems, various model mismatches are present in the sample covariance matrix and signal steering vector, which severely deteriorates the performance of the beamformer. To address this issue, we develop a novel adaptive beamforming algorithm in which the weight vector is a linear combination of the presumed steering vector and basis vectors of the orthogonal supplement subspace of the steering vector. Then, we convert the common constrained optimization problem into an unconstrained one to establish the new beamformer. Moreover, we use damped singular value decomposition regularization, which employs the L-curve method to adaptively determine the regularized factor (loading level), for suppressing the effects of model mismatches. In addition, we perform simulations of the proposed algorithm and other existing beamforming algorithms by considering several commonly encountered mismatches (e.g., the direction-of-arrival mismatch, sensor gain, phase error and location perturbation, coherent local scattering, and mutual coupling) and demonstrate the superior performance of the new beamforming algorithm.

URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming

Abstract: The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadrature (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix reconstruction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms is relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining accuracy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiments demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.

Review on Beamforming Techniques for Millimeter Wave Massive MIMO

Abstract: To meet the demands of the increasing wireless data traffic, there is a need to explore the new spectrum region to meet the demands. This leads to the exploration of the millimeter wave band which will be able to handle the traffic. The principle target of this paper is to give survey of the diverse beamforming procedures for the massive MIMO framework. Classification and comparison of different optimal beamforming techniques are reviewed for energy as well as for spectral efficiency, throughput, inter-cell, and intra-cell interference to decide which technique gives the better performance for the millimeter Wave massive MIMO system. By analyzing different beamforming algorithms, an optimal beamforming algorithm is suggested that will enhance massive MIMO system performances and also satisfy the demands of future generation of wireless communication system.

Generalized sidelobe canceler beamforming combined with Eigenspace-Wiener postfilter for medical ultrasound imaging

Abstract: The beamforming algorithm is key to the image quality of the medical ultrasound system. The generalized sidelobe canceler (GSC) beamforming can improve the image quality in lateral resolution, but the contrast is not improved correspondingly.In our research, we try to optimize the generalized sidelobe canceler to obtain images that achieve an improvement in both lateral resolution and contrast.We put forward a new beamforming algorithm which combines the generalized sidelobe canceler and Eigenspace-Wiener postfilter. According to eigenspace decomposition of the covariance matrix of the received data, the components of the Wiener postfilter can be calculated from the signal matrix and the noise matrix. Then, the adaptive weight vector of GSC is further constrained by the Eigenspace-Wiener postfilter, which make the output energy of the receiving array closer to the desired signal than the conventional GSC output.We compare the new beamforming algorithm with delay-and-sum (DS) beamforming, synthetic aperture (SA) beamforming, and GSC beamforming using the simulated and experimental data sets. The quantitative results show that our method reduces the FWHM by 85.5%, 80.5%, and 38.9% while improving the CR by 123.6%, 47.7%, 84.4% on basis of DS, SA, and GSC beamforming, respectively.The new beamforming algorithm can obviously improve the imaging quality of medical ultrasound imaging systems in both lateral resolution and contrast.