Adaptive Beamforming Research Articles

Adaptive Beamforming for On-Orbit Satellite-Based ADS-B Based on FCNN.

Digital multi-beam synthesis technology is generally used in the on-orbit satellite-based Automatic Dependent Surveillance-Broadcast (ADS-B) system. However, the probability of successfully detecting aircraft with uneven surface distribution is low. An adaptive digital beamforming method is proposed to improve the efficiency of aircraft detection probability. The current method has the problem of long operation time and is not suitable for on-orbit operation. Therefore, this paper proposes an adaptive beamforming method for the ADS-B system based on a fully connected neural network (FCNN). The simulation results show that the calculation time of this method is about 2.6 s when more than 15,000 sets of data are inputted, which is 15-80% better than the existing methods. Its detection success probability is 10% higher than those of existing methods, and it has better robustness against large amounts of data.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.

Expanding generalized contrast-to-noise ratio into a clinically relevant measure of lesion detectability by considering size and spatial resolution.

Early image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, invalidating the meaning of those metrics. The result is that beamformers may "manipulate" metrics without producing more clinical information. In this work, Smith etal.'s signal-to-noise ratio (SNR) metric for lesion detectability is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses generalized contrast-to-noise ratio (gCNR) as a core. It is analytically shown that for Rayleigh distributed data, gCNR is a function of Smith etal.'s (and therefore can be used as a substitution). More robust methods for estimating the resolution cell size are considered. Simulated lesions are included to verify the equations and demonstrate behavior, and it is shown to apply equally well to in vivo data. gSNR is shown to be equivalent to SNR for delay-and-sum (DAS) beamformed data, as intended. However, it is shown to be more robust against transformations and report lesion detectability more accurately for non-Rayleigh distributed data. In the simulation included, the SNR of DAS was , and minimum variance (MV) was , but the gSNR of DAS was , and MV was , which agrees with the subjective assessment of the image. Likewise, the transformation (which is clinically identical to DAS) had an incorrect SNR of and a correct gSNR of . Similar results are shown in vivo. Using gCNR as a component to estimate gSNR creates a robust measure of lesion detectability. Like SNR, gSNR can be compared with the Rose criterion and may better correlate with clinical assessments of image quality for modern beamformers.

Super-Resolution Processing for Multiple Aperture Antenna to Suppress Multipath

Angle estimation for low-altitude targets above the sea surface is a challenging problem due to multipath interference from surface reflection signals, and various approaches have been proposed. This paper proposes a matrix pencil method with multiple apertures. The matrix pencil method effectively responds to dynamic scenarios because it performs better when using a single snapshot than other methods. Also, employing multiple apertures is more economical than using one large aperture. Therefore, we propose a computationally efficient approach using this method and structures. The proposed two-stage MP method incrementally improves the resolution in two stages: in stage 1, we extract the denoised signals at each aperture level, and in stage 2, we further improve the resolution with those signals. In comparison with the angular resolution defined by the half-power beamwidth (HPBW) of a uniform linear array (ULA) antenna with an equivalent number of arrays, the proposed method demonstrated a superior resolution of less than 0.087 of the HPBW at a high signal-to-noise ratio (SNR) of 40 dB, and less than 0.31 of it even at a relatively low SNR of 15 dB, based on 90% of the resolving probability. For the multipath problem, the proposed scheme has the advantage of not requiring prior geometric information, and its performance is demonstrated through simulations to be better than the adaptive beamforming method and the composite monopulse method.

A deep learning‐based channel estimation and joint adaptive hybrid beamforming approach for mm‐wave massive MIMO with group‐of‐subarrays

SummaryMillimeter‐wave massive multiple‐input‐multiple‐output (MIMO) is a wireless communication that enables communication at extremely high data rates. It is widely used to expand communication bandwidth to improve spectral and energy efficiency. The mm‐wave system is usually based on the radio frequency (RF) chain. Beamforming is used to minimize the interface signal and maximize each user's received signal power. The hybrid beamforming method uses channel estimation and beam generation techniques that improve overall performance and save energy. There are various limitations and disadvantages arising from different techniques of the MIMO system. Hence, the proposed model is introduced to enhance the performance and maximize the spectral and energy efficiency. Initially, the optimal RF chain selection and channel optimization are done by the differential evolution firefly‐assisted optimized channel compression‐reconstruction (DEF_OCCR) network. In this research, the RF chain selection process is done using hybrid differential evolution firefly optimization. A robust autoencoder‐driven deep learning model is proposed for channel estimation after RF chain selection. Finally, the deep learning convolutional joint adaptive hybrid beamforming network (DConvHB) is used for beamforming to maximize spectral efficiency. The overall performance of this work is analyzed using several existing models to describe its superiority. The average system throughput is 12.85 bits, and the spectral efficiency of the proposed model is 5 bits for 10 dB and 32.18 bits for 13 dB. The proposed energy efficiency model is 1.73 bits and 4.96 bits for 10 dB.

Generalized sidelobe canceller based adaptive multiple-input multiple-output radar array beamforming under scenario mismatches

It is well known that the performance of an adaptive MIMO radar array fully depends on the precise steering control and is deteriorated by even a small scenario mismatch. This paper presents an advanced generalized sidelobe canceller (AGSC) based adaptive MIMO radar array beamformer with robustness against the effect due to scenario mismatches. A new signal blocking matrix is developed for effectively blocking the desired signal when the adaptive beamforming is performed under multiple scenario mismatches. The novelty of the new signal blocking matrix is that it contains two additional matrix components in addition to the conventional blocking matrix. The first one is a matrix made up of the basis orthogonal to some appropriately designed derivative constraint vector. It avoids the possible leakage of the desired signal due to scenario mismatches. The other one is a matrix made up of the dominant eigenvectors associated with the correlation matrix of the blocked data vector at the output of the first matrix component. It is employed to preserve all of the interference signals. As a result, the whole blocking operation can delete the desired signal and save the interference signals under multiple scenario mismatches. Hence, the AGSC based adaptive MIMO radar beamformer effectively deals with the performance degradation caused by scenario mismatches without resorting to any robust optimization algorithms. Performance analysis and complexity evaluation regarding the AGSC based adaptive MIMO radar beamformer are presented. Simulation results are also provided for confirmation and comparison.

Fully Complex-Valued Gated Recurrent Neural Network for Ultrasound Imaging.

Ultrasound imaging is widely used in medical diagnosis. It has the advantages of being performed in real time, cost-efficient, noninvasive, and nonionizing. The traditional delay-and-sum (DAS) beamformer has low resolution and contrast. Several adaptive beamformers (ABFs) have been proposed to improve them. Although they improve image quality, they incur high computation cost because of the dependence on data at the expense of real-time performance. Deep-learning methods have been successful in many areas. They train an ultrasound imaging model that can be used to quickly handle ultrasound signals and construct images. Real-valued radio-frequency signals are typically used to train a model, whereas complex-valued ultrasound signals with complex weights enable the fine-tuning of time delay for enhancing image quality. This work, for the first time, proposes a fully complex-valued gated recurrent neural network to train an ultrasound imaging model for improving ultrasound image quality. The model considers the time attributes of ultrasound signals and uses complete complex-number calculation. The model parameter and architecture are analyzed to select the best setup. The effectiveness of complex batch normalization is evaluated in training the model. The effect of analytic signals and complex weights is analyzed, and the results verify that analytic signals with complex weights enhance the model performance to reconstruct high-quality ultrasound images. The proposed model is finally compared with seven state-of-the-art methods. Experimental results reveal its great performance.

Adaptive Beamforming Algorithm Based on Residual Neural Networks

Adaptive Beamforming Algorithm Based on Residual Neural Networks

Wideband Adaptive Beamforming via Multiscale Channel Attention Convolutional Neural Network

Wideband Adaptive Beamforming via Multiscale Channel Attention Convolutional Neural Network

Robust adaptive beamforming for cylindrical uniform conformal arrays based on low-rank covariance matrix reconstruction

Recently, conformal arrays have attracted considerable interest because such arrays can provide reduced radar cross-section and increased angle coverage. In this article, we devise a robust adaptive beamforming (RAB) approach using cylindrical uniform conformal array (CUCA). Firstly, we derive the minimum variance distortionless response (MVDR) beamformer for the CUCA by utilizing the noise subspace of interference covariance matrix (ICM) and steering vector (SV) of the signal-of-interest (SOI). Subsequently, the ICM is reconstructed by estimating the noise-free covariance matrix of the CUCA outputs and the interference projection matrix. Specifically, the noise-free covariance matrix can be regarded as multiple low-rank covariance matrices, and each low-rank matrix is reconstructed by formulating a nuclear norm minimization (NNM) problem. With the reconstructed covariance matrix, the 2-D DOAs of sources are determined by employing 2-D MUSIC spectrum to form the interference projection matrix. In addition, the SOI SV is estimated by solving a quadratically constrained quadratic programming (QCQP) problem. Numerical results demonstrate that the proposed approach is obviously superior to the existing RAB techniques.

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution.

Identification of seizure sources in the brain is of paramount importance, particularly for drug-resistant epilepsy patients who may require surgical operation. Interictal epileptiform discharges (IEDs), which may or may not be frequent, are known to originate from seizure networks. Delayed responses (DRs) to brain electrical stimulation have been recently discovered. If DRs and IEDs come from the same location and the DRs can be accurately localized, there will be a significant step in identification of the seizure sources. The solution to this important question has been investigated in this paper. For this, we have exploited the morphology of these spike-type events, as well as the variability in their temporal locations, to develop new constraints for an adaptive Bayesian beamformer that outperforms the conventional and recently proposed beamformers even for identifying correlated sources. This beamformer is applied to an array (a.k.a mat) of cortical EEG electrodes. The developed approach has been tested on 300 data segments from five epileptic patients included in this study, which clinically represent a large population of candidates for surgical treatment. As the significant outcome of applying this beamformer, it is very likely (if not certain) that for an epileptic subject, the IEDs and DRs originate from the same location in the brain. This paves the way for a quick identification of the source(s) of seizure in the brain.

Improved subarray coherence factor beamformer for ultrasound plane-wave imaging

Abstract Coherent plane-wave compounding (CPWC) has gained significant traction in medical ultrasound imaging. The coherence factor (CF) weighting algorithm, a classical adaptive beamforming strategy, aims to enhance the CPWC imaging lateral resolution, but it also noticeably compromises the speckle patterns, particularly.at a low signal-to-noise ratio (SNR). Delay multiply and sum (DMAS) has been proposed to reduce side lobes. However, the reconstructed image quality with DMAS is limited in terms of resolution and speckle level. In the present study, an improved subarray coherence factor (ISCF) algorithm is introduced by combining CF with DMAS strategies, aiming to improve the contrast and resolution while preserving the speckle level. The performance of the proposed algorithm is evaluated using simulation and phantom datasets. Results indicate that the resolution and the contrast of ISCF can be improved by 41.6% and 79.4%, respectively; compared with delay and sum (DAS) algorithm. Additionally, the resolution and the speckle level of ISCF can be improved by 10.6% and 35.7% compared with CF algorithm, respectively. The proposed ISCF algorithm outperforms CF in terms of resolution, contrast, and speckle preservation.

Vessel recovery using ultrasound localisation microscopy: An in silico comparative study between minimum variance and delay-and-sum beamformers

The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3–4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming.

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency.

Adaptive beamforming approach for secure communication in 5G network

Adaptive beamforming approach for secure communication in 5G network

Enhancing efficiency in spaceborne phased array systems: MVDR algorithm and FPGA integration

Digital Beamforming (DBF) has garnered significant attention within the space community, driven by the heightened flexibility offered by satellites equipped with active antennas for diverse applications like spaceborne synthetic aperture radar (SAR) systems and communication services. This paper presents a comprehensive exploration encompassing analysis, design, and implementation of a Minimum Variance Distortionless Response (MVDR) algorithm tailored for application in a Digital Beamforming Receiver system. The described system leverages the capabilities of the high-speed Analog to Digital Converter (ADC) device EV12AQ600, space-qualified, and the high-performance FPGA XCKU085. These components are seamlessly integrated into the EV12AQ60X-ADX-EVM evaluation board, thoughtfully provided by Teledyne e2v Semiconductors. The design methodology outlined herein is driven by the overarching objective to minimize FPGA resource utilization and power consumption for spaceborne phased array systems. This objective is chiefly met through the implementation of a systolic array structure adept at processing both real and complex input samples. This innovative approach results in a substantial reduction in allocated resources compared to conventional architectures. Furthermore, the design incorporates the use of the CORDIC algorithm to compute the inverse of the correlation matrix, obviating the need for square root and division operations. This strategic utilization of algorithms enhances the system's efficiency in terms of resource utilization, computational load, and processing time. To validate the anticipated behavior of the adaptive beamforming system, various radio frequency (RF) input signals are applied to the system, which is physically instantiated on the EV12AQ60X-ADX-EVM evaluation board. The experimental results affirm the efficacy of the proposed design, underscoring its suitability for spaceborne applications.

Antenna resource allocation for netted radar system with main-lobe deceptive jamming suppression

An adaptive beamforming method based on the polarimetric scattering matrix (PSM) of targets to suppress deceptive jamming located in the beam pattern main lobe is proposed in this study. For the netted radar system, the true/false mixed targets of different/same range ambiguity region are distinguished by the range-PSM dimension; the flexible allocation of antenna resource is realised by the number of estimated targets; the main-lobe deceptive jamming is also suppressed by the adaptive beamforming method. First, a polarimetric multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA) was considered as the initial antenna configuration. Subsequently, the performance of the transmit–receive spatial frequency was analysed, and the characteristics of false targets in the polarimetric FDA-MIMO radar were discussed. We then considered the optimisation problem between the transmit polarisation and the frequency increment, as well as the allocation of antenna resources. Finally, an adaptive beamforming method was utilised to achieve deceptive jamming suppression in the main lobe. The effectiveness of PSM was verified with respect to identifying true/false targets, and the jamming suppression performance was evaluated in the main lobe by comparing it with existing systems via simulation results.

Adaptive complementary neighboring sub-aperture beamforming for thermoacoustic imaging.

When applied to thermoacoustic imaging (TAI), the delay-and-sum (DAS) algorithm produces strong sidelobes due to its disadvantages of uniform aperture weighting. As a result, the quality of TAI images recovered by DAS is often severely degraded by strong non-coherent clutter, which restricts the development and application of TAI. To address this issue, we propose an adaptive complementary neighboring sub-aperture (NSA) beamforming algorithm for TAI. In NSA, we introduce a coordinate system transformation when calculating the normalized cross-correlation (NCC) matrix. This approach enables the computation of the NCC coefficient within the specified kernel without complex coordinate calculations. We first conducted the numerical simulation experiment to validate NSA using a tree branch phantom. In addition, we also conducted phantom (five sauce tubes), ex vivo (ablation needle in ex vivo porcine liver), and in vivo (human arm) TAI experiments using our TAI system with a center frequency of 3GHz. In the numerical simulation experiment, the structural similarity index (SSIM) value for NSA is increased from 0.37828 for DAS to 0.75492. In the point target phantom TAI experiment, the generalized contrast-to-noise ratio (gCNR) value for NSA is increased from 0.936 for DAS to 0.962. The experimental results show that NSA can recover clearer thermoacoustic images compared to DAS. In the ex vivo TAI experiment, the full width at half maxima (FWHM) of an ablation needle (diameter=1.5mm) for coherence factor (CF) weighted DAS and NSA are 0.9 and 1.3mm, respectively. Furthermore, in the in vivo TAI experiment, CF reduces the signals within the arm compared to NSA. Therefore, compared with CF, NSA can maintain the integrity of target information in TAI while effectively suppressing non-coherent background clutter. NSA can effectively reduce non-coherent background noise while ensuring the completeness of the target information. So, NSA offers the potential to provide high-quality thermoacoustic images and further advance their clinical application.

Adaptive Hybrid Beamforming for UAV mmWave Communications Against Asymmetric Jitter

Adaptive Hybrid Beamforming for UAV mmWave Communications Against Asymmetric Jitter

How Often Channel Estimation is Required for Adaptive IRS Beamforming: A Bilevel Deep Reinforcement Learning Approach

How Often Channel Estimation is Required for Adaptive IRS Beamforming: A Bilevel Deep Reinforcement Learning Approach