Adaptive Beamforming Method 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.

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.

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.

Robust adaptive beamforming with partly calibrated arrays in the presence of gain-phase errors

The performance of the beamformer, especially the beamformer based on the interference plus noise covariance matrix (INCM) reconstruction which needs accurate array manifold knowledge, will degrade dramatically in the presence of gain-phase errors. To solve this problem, we propose a robust adaptive beamforming method based on partly calibrated arrays (PCA). The main idea of this method is to estimate the gain-phase errors more accurately using the higher signal-to-noise ratio (SNR) signal sources and the PCA. After that, we calibrate the interference signal steering vector (SV) and reconstruct the more accurate INCM based on the estimation results. Finally, the SV of the desired signal is estimated by the intersection of two special subspaces. On the basis of the above research, the optimal weight vector is obtained. Numerical simulation results demonstrate the superior performance of the proposed beamformer relative to other existing beamformers in the presence of gain-phase errors.

Simple subspace based adaptive beamforming under Toeplitz covariances

When uncorrelated signals are incident on a uniform linear array, the array covariance matrix is of the Toeplitz form. An adaptive beamforming method is proposed based on the signal-plus-interference (SI) subspace via the Toeplitz rectification of the sample matrix. The rectified matrix is shown to be more accurate in a norm sense than the modified matrix according to the centro-Hermitian property. Since the former also is centro-Hermitian we can efficiently obtain its eigen-decomposition from a real matrix and then the weight vector in the estimated SI subspace. The proposed method, showing robustness to pointing errors, is not only computationally efficient but also very quickly converges to the optimum performance as demonstrated in the simulation.

Realization of adaptive beamformer on open-source hardware

Realization of an efficient system for extracting useful audio signals in the presence of interferences is an important engineering problem in the field of acoustics. This paper proposes a method for extracting sound signals from a specific direction of incidence. A microphone array consisting of eight microphones was used, coupled with space-time signal processing executed in real time on hardware. The microphone signals are filtered by an adaptive beamformer, whose coefficients are optimized in real time on open-source hardware using the Least Mean Square algorithm. This paper describes the characteristics and limitations of the used microphone array. Testing the system through simulation indicates that extracting narrowband and wideband signals using the presented adaptive beamforming method is theoretically possible. This paper aims to experimentally evaluate the algorithm under real-world conditions, as well as identify the limitations of extracting wideband signals. Adaptation of the filter coefficients and filtering the signals from the microphones was implemented on Bela hardware, specialized for processing audio signals. This experiment highlights the limitations that arise from extracting a wideband signal using this type of hardware in real conditions. By comparing the useful and filtered signals in both time and frequency domains, the quality of the filtering was analyzed. Our experiments suggest that the current hardware specifications are a limiting factor for successful wideband signal filtering in real conditions, as they limit the maximum possible order of the adaptive filter, which proved to be insufficient.

Three DOF Rotation Compensation in Floating HFSWR for Ocean Current Inversion

High-frequency surface wave radar (HFSWR) has been widely used for ocean surface current inversion. Floating this radar can realize the ocean surface inversion among large areas far away from the coastline. However, six degree-of-freedom (DOF) movements will set the barrier for the ocean current inversion on its radial velocity estimation and direction of arrival estimation. Among these movements, three DOF rotations can cause both Doppler modulation and azimuth modulation while three DOF translations only cause Doppler modulation which is dependent on the arrival direction of echoes. Hence, the analysis and compensation for three DOF rotations are first to be solved. In this paper, we analyse the Doppler modulation and azimuth modulation of three DOF rotations and further develop the global adaptive beamforming method to compensate for these rotations. Adaptive beamforming is used to compensate for rotations to make the real-time beams fixed to the ground when the platform is rotating. Simulations indicate that the global adaptive beamforming can perfectly compensate for the reasonable rotations, among which the yaw rotation is the dominant factor for both Doppler spectrum modulation and azimuth modulation. The rotation compensation using global adaptive beamforming can greatly improve the precision of ocean surface inversion.

Application of adaptive finite focus beamforming method for localization of low-frequency transformer sound

In this paper, the developed adaptive linearly constrained minimum variance finite focus beamforming method is used to increase the accuracy and resolution of low-frequency transformer sound source localization. In the first step, simulations on virtual arrays are conducted to define the array size and compare the influence of three different beamforming algorithms on sound localization accuracy and resolution. In the second step, measurements of the regulating transformer as a noise source are conducted in the semi-anechoic chamber. A large rectangular microphone array, with 90 microphone positions spaced 0.44 meters apart, is used for that purpose. The developed algorithm is compared with infinite and finite focus beamforming for the localization of total noise and noise at specific noise harmonics.

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound.

Ultrafast ultrasound has recently emerged as an alternative to traditional focused ultrasound. By virtue of the low number of insonifications it requires, ultrafast ultrasound enables the imaging of the human body at potentially very high frame rates. However, unaccounted for speed-of-sound variations in the insonified medium often result in phase aberrations in the reconstructed images. The diagnosis capability of ultrafast ultrasound is thus ultimately impeded. Therefore, there is a strong need for adaptive beamforming methods that are resilient to speed-of-sound aberrations. Several of such techniques have been proposed recently but they often lack parallelizability or the ability to directly correct both transmit and receive phase aberrations. In this article, we introduce an adaptive beamforming method designed to address these shortcomings. To do so, we compute the windowed Radon transform of several complex radio-frequency images reconstructed using delay-and-sum. Then, we apply to the obtained local sinograms weighted tensor rank-1 decompositions and their results are eventually used to reconstruct a corrected image. We demonstrate using simulated and in-vitro data that our method is able to successfully recover aberration-free images and that it outperforms both coherent compounding and the recently introduced SVD beamformer. Finally, we validate the proposed beamforming technique on in-vivo data, resulting in a significant improvement of image quality compared to the two reference methods.

Improved eigenspace-based method for robust adaptive beamforming with dimension search

The eigenspace-based technique, as a classic method for robust adaptive beamforming (RAB), can substantially mitigate the negative impact caused by model mismatch. However, the conventional eigenspace-based beamformer (CESB) undergoes severe performance degradation at low signal-to-noise ratio (SNR). This is because it firmly sets the dimension of the projection subspace (DOPS) to the source number. To alleviate this problem, an improved eigenspace-based approach is proposed in this communication, where the DOPS is auto-selected to adapt to different cases. We roughly analyze the rationality that the robustness of the eigenspace-based technique can be enhanced by simply adjusting the DOPS at low SNR. In addition, we concretely propose an automatic selection method for the DOPS derived from a minimum beamformer sensitivity estimation problem. The proposed method is advantageous in that it is parameter-free. Moreover, simulation experiments are performed to demonstrate the improvement of the CESB and its robustness against steering vector mismatch.

Adaptive Beamforming with Small Samples and Low Complexity for Large-Scale Arrays

In this paper, we propose a novel adaptive beamforming method based on interference plus noise covariance matrix (INCM) reconstruction with few samples and low complexity. Unlike existing techniques, we make use of the fact that large-scale antenna arrays can significantly increase the spectral analysis’s resolution by estimating the steering vector (SV) and power of the interfering signal using the discrete Fourier transform (DFT). In addition, a simple spatial rotation operation is employed to resolve the power “leakage” problem of the DFT spectrum and increase estimation precision. The proposed method achieves a high output SINR by reconstructing an accurate INCM with the advantage of simple implementation and low computation complexity. Finally, we verify the effectiveness of the proposed method by numerical simulation and result analysis.

Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

Abstract Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

DRFM Repeater Jamming Suppression Method Based on Joint Range-Angle Sparse Recovery and Beamforming for Distributed Array Radar

Distributed array radar achieves high angular resolution and measurement accuracy, which could provide a solution to suppress digital radio frequency memory (DRFM) repeater jamming. However, owing to the large aperture of a distributed radar, the far-field plane wave assumption is no longer satisfied. Consequently, traditional adaptive beamforming methods cannot work effectively due to mismatched steering vectors. To address this issue, a DRFM repeater jamming suppression method based on joint range-angle sparse recovery and beamforming for distributed array radar is proposed in this paper. First, the steering vectors of the distributed array are reconstructed according to the spherical wave model under near-field conditions. Then, a joint range-angle sparse dictionary is generated using reconstructed steering vectors, and the range-angle position of jamming is estimated using the weighted L1-norm singular value decomposition (W-L1-SVD) algorithm. Finally, beamforming with joint range-angle nulling is implemented based on the linear constrained minimum variance (LCMV) algorithm for jamming suppression. The performance and effectiveness of proposed method is validated by simulations and experiments on an actual ground-based distributed array radar system.

Defect Detection Algorithm for Wing Skin with Stiffener Based on Phased-Array Ultrasonic Imaging.

In response to the real-time imaging detection requirements of structural defects in the R region of rib-stiffened wing skin, a defect detection algorithm based on phased-array ultrasonic imaging for wing skin with stiffener is proposed. We select the full-matrix-full-focusing algorithm with the best imaging quality as the prototype for the required detection algorithm. To address the problem of poor real-time performance of the algorithm, a sparsity-based full-focusing algorithm with symmetry redundancy imaging mode is proposed. To address noise artifacts, an adaptive beamforming method and an equal-acoustic-path echo dynamic removal scheme are proposed to adaptively suppress noise artifacts. Finally, within 0.5 s of imaging time, the algorithm achieves a detection sensitivity of 1 mm and a resolution of 0.5 mm within a single-frame imaging range of 30 mm × 30 mm. The defect detection algorithm proposed in this paper combines phased-array ultrasonic technology and post-processing imaging technology to improve the real-time performance and noise artifact suppression of ultrasound imaging algorithms based on engineering applications. Compared with traditional single-element ultrasonic detection technology, phased-array detection technology based on post-processing algorithms has better defect detection and imaging characterization performance and is suitable for R-region structural detection scenarios.

Robust Adaptive Beamforming Based on a Convolutional Neural Network

To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage convolutional neural network. The first stage includes convolutional blocks and residual blocks without downsampling; the blocks assess the covariance matrix precisely using finite snapshots. The second stage maps the first stage’s output to an adaptive weight vector employing a similar structure to the first stage. The two stages are pre-trained with different datasets and fine-tuned as end-to-end networks, simplifying the network training process. The two-stage structure enables the network to possess practical physical meaning, allowing for satisfying performance even with a few snapshots in the presence of array G/P errors. We demonstrate the resulting beamformer’s performance with numerical examples and compare it to various other adaptive beamformers.

Robust adaptive beamforming via covariance matrix reconstruction with diagonal loading on interference sources covariance matrix

The majority of the existing adaptive beamforming methods utilize the interference-plus-noise covariance matrix (INCM) to prevent the signal of interest (SOI) from being suppressed. However, the removal of the SOI component will lead to a decrease in the performance at low input signal-to-noise ratio (SNR). In this paper, a novel interference sources covariance matrix (ISCM) diagonal loading method is proposed to improve the robustness of the beamformer. First of all, we employ the peaks of the Capon spectrum to calculate the corresponding nominal steering vectors (SVs), and then accurate SVs of the SOI and interferences are obtained by subspace projection. Instead of utilizing the orthogonality, the proposed method preserves the non-orthogonality between SVs to estimate an accurate signal-plus-interference sources covariance matrix (SISCM), and then obtains a reconstructed array covariance matrix (RACM). Furthermore, we conduct diagonal loading on ISCM and construct a modified array covariance matrix (MACM) to keep the interference-to-signal ratio (ISR) after loading at a proper large value. Simulation results demonstrate the robustness of the proposed method, and indicate that the proposed method not only has the low computational complexity but also performs well at both low and high input SNR.

Improving lateral resolution and contrast by combining coherent plane-wave compounding with adaptive weighting for medical ultrasound imaging

Due to the severe lateral lobe artifact by coherent plane-wave compounding (CPWC) and the low signal-to-noise ratio of radiofrequency (RF) data collected from the plane wave, the adaptive beamforming methods based on focused wave imaging (FWI) are improper to be directly applied to CPWC. To obtain a high-quality image with high resolution and contrast, this study combined the threshold phase coherence factor (THR-PCF) with the reconstructed covariance matrix minimum variance (RCM-MV) and then proposed a novel CPWC-based adaptive beamforming algorithm, THR-PCF + RCM-MV. The simulation, phantom, and in-vivo experiments were performed to investigate the performance of the proposed methods in comparison with the CPWC and the classical adaptive methods including the minimum variance (MV), generalized coherence factor (GCF) and their combination GCF + MV. The simulation results demonstrated that the THR-PCF + RCM-MV beamformer improved contrast ratio (CR) by 28.14%, contrast noise ratio (CNR) by 22.01%, speckle signal-to-noise ratio (s_SNR) by 23.58%, generalized contrast-to-noise ratio (GCNR) by 0.3%, and the full width at half maximum (FWHM) by 43.38% on average, compared with the GCF + MV method. The phantom experimental results showed a better performance of the THR-PCF + RCM-MV beamformer with an average improvement by 21.95% in CR, 2.62% in s_SNR, and 48.64% in FWHM compared with the GCF + MV. Meanwhile, the results showed that the image quality of the near and far fields was enhanced by the THR-PCF + RCM-MV. The in-vivo imaging results showed that our new method had potential for clinical application. In conclusion, the lateral resolution and contrast of medical ultrasound imaging could be improved greatly with our proposed method.

An Ultrasonic Adaptive Beamforming Method and Its Application for Trans-Skull Imaging of Certain Types of Head Injuries; Part II: Reception Mode and Adaptive Imaging.

Background theory and a new algorithm for single-point adaptive focusing in transmission mode through ultrasonic barriers via one-dimensional phased arrays were reported in part I. In this paper the algorithm is further extended and implemented into a full adaptive beamforming process, including complete transmission and reception modes. Corrected time delay patterns, adapted to the local acoustical and geometrical properties of the barrier, are calculated and applied in both modes. Further, an adaptive imaging process is also developed that implements the proposed beamforming process for two-dimensional imaging through randomly shaped multilayered phase-aberrating structures. The method is optimized for the case of human skull as the ultrasound barrier and its application for transcranial imaging is discussed. Laboratory results of adaptive imaging through realistic skull-mimicking phantoms are presented. The algorithms are implemented on a 64-channel ultrasound open-source phased array platform controlling a standard 128-element biomedical phased array. Irregularly shaped reflectors with characteristic dimensions of the order of ∼0.5 mm to ∼4.5 mm were used as targets behind the skull phantoms in our experiments. Minimum and maximum distortional target displacements of 2.2 mm and 25.3 mm (in 12 cm depth) were observed in sonograms when uncompensated time delays were used. By contrast, the positioning errors ranged from 0.0 to 0.9 mm when our algorithm was employed. The adaptive imaging results demonstrate strong potential of the proposed technique for diagnostic imaging of acoustically reflective head injuries directly through intact human skull.

Ultrasound Ultrafast Power Doppler Imaging with High Signal-to-Noise Ratio by Temporal Multiply-and-Sum (TMAS) Autocorrelation.

Coherent plane wave compounding (CPWC) reconstructs transmit focusing by coherently summing several low-resolution plane-wave (PW) images from different transmit angles to improve its image resolution and quality. The high frame rate of CPWC imaging enables a much larger number of Doppler ensembles such that the Doppler estimation of blood flow becomes more reliable. Due to the unfocused PW transmission, however, one major limitation of the Doppler estimation in CPWC imaging is the relatively low signal-to-noise ratio (SNR). Conventionally, the Doppler power is estimated by a zero-lag autocorrelation which reduces the noise variance, but not the noise level. A higher-lag autocorrelation method such as the first-lag (R(1)) power Doppler image has been developed to take advantage of the signal coherence in the temporal direction for suppressing uncorrelated random noises. In this paper, we propose a novel Temporal Multiply-and-Sum (TMAS) power Doppler detection method to further improve the noise suppression of the higher-lag method by modulating the signal coherence among the temporal correlation pairs in the higher-lag autocorrelation with a tunable pt value. Unlike the adaptive beamforming methods which demand for either receive-channel-domain or transmit-domain processing to exploit the spatial coherence of the blood flow signal, the proposed TMAS power Doppler can share the routine beamforming architecture with CPWC imaging. The simulated results show that when it is compared to the original R(1) counterpart, the TMAS power Doppler image with the pt value of 2.5 significantly improves the SNR by 8 dB for the cross-view flow velocity within the Nyquist rate. The TMAS power Doppler, however, suffers from the signal decorrelation of the blood flow, and thus, it relies on not only the pt value and the flow velocity, but also the flow direction relative to the geometry of acoustic beam. The experimental results in the flow phantom and in vivo dataset also agree with the simulations.

Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming

Adaptive beamforming can efficiently contract interference and noise. Due to high sensitivity of the beamformer to model mismatch, the capability of interference reduction will critically degrade when the signal model mismatch occurs, particularly when the sampling sequence contains the desired signal. For the purpose of enhancing the robustness of beamformers to signal model mismatch, we propose a new robust adaptive beamforming (RAB) method. Firstly, the precise steering vector (SV) associating with the desired signal is estimated by employing the minimum norm of subspace projection (MNSP) approach. Secondly, the nominal interference SVs are estimated via the maximum entropy power spectrum. Subsequently, the corrected interference SVs and powers are obtained by oblique projection. Finally, the interference-plus-noise covariance matrix (INCM) is reconstructed, and the proposed RAB is obtained. Multiple simulations are carried out and demonstrate the robustness of the proposed RAB method.