Adaptive Beamforming Techniques Research Articles

Advanced Interference Mitigation Method Based on Joint Direction of Arrival Estimation and Adaptive Beamforming for L-Band Digital Aeronautical Communication System

The L-band digital aeronautical communication system (LDACS) is one of the candidate technologies for future broadband digital aeronautical communications, utilizing the unused L-band spectrum between distance measuring equipment (DME) channels. However, the higher signal power of DME complicates LDACS implementation. This paper proposes an advanced DME mitigation approach for the LDACS, integrating joint direction of arrival (DOA) estimation with adaptive beamforming techniques. The proposed method begins by exploiting the cyclostationary characteristics of signals, accurately obtaining the preliminary direction of the LDACS signal using the Cyclic-MUSIC method. Subsequent precise steering vectors (SVs) are selected through Capon spectrum search, followed by the reconstruction of the interference plus noise covariance matrix (INCM). Using the obtained SV and INCM, the weight vector is calculated and beamforming is performed. Simulation results validate that the proposed method not only accurately estimates the direction of LDACS signal but also efficiently mitigates DME interference, demonstrating a superior performance and reduced algorithmic complexity, even in scenarios with lower signal-to-noise ratios (SNRs) and the presence of DOA estimation errors. Additionally, the proposed method achieves a low bit error rate (BER), further validating its ability to ensure communication quality and enhance the reliability of LDACS.

Orthogonal beamforming technique for massive MIMO systems

AbstractBeamforming represents a pivotal technology in massive multiple-input multiple-output (MIMO) systems, as it facilitates the regulation of transmission and reception operations. Beamforming techniques’ categorization is based either on their hardware architecture or implementation strategy. This paper proposes an orthogonal beamforming technology founded on a specific implementation method that utilizes predetermined orthogonal beams to serve users. The suggested approach incorporates numerous orthogonal beams relying on a substantial number of antennas at the base station. The primary objective of this approach is to enhance the performance of massive MIMO systems by augmenting spectral efficiency and accommodating more users. The proposed beamforming approach is well suited for millimeter frequency bands. The purpose of this paper is to explore the suggested orthogonal beamforming technology. The concept of this approach is described at first and then followed by an evaluation of its efficacy for a single user through the allocation of orthogonal beams. The suggested approach is also examined in the context of multiuser systems, and the results are compared with the adaptive ZF beamforming technique. Furthermore, the paper presents solutions to the issues that may arise in multiuser systems, for example, ensuring that each orthogonal beam is assigned to only one user. The simulations conducted in this study demonstrate that the suggested approach outperforms the ZF technique in terms of both the spectral efficiency and the number of serviced users. Specifically, the suggested approach can enhance SE by approximately 40.6% over the ZF technique, and it can support up to double the number of users when compared to the ZF approach.

Navigating The Complexity Overcoming Mobility Obstacles in Massive MIMO Systems with MIMO Antennas.

This research investigates the synergy between Massive Multiple Input, Multiple Output (MIMO) systems and mobility challenges, with a focus on the pivotal role played by Multiple Antenna Systems (MIMO antennas). Employing a sophisticated experimental setup featuring a 3.5 GHz Massive MIMO base station equipped with 64 MIMO antennas, mobile terminals on motorized platforms, and advanced RF measurement tools, the study explores adaptive beamforming, spatial multiplexing, and diversity techniques. Results from 100 experiments, each lasting 10 minutes, reveal that adaptive beamforming dynamically improves user tracking in response to mobility-induced channel variations. Spatial multiplexing and diversity techniques enhance throughput and reliability, especially in high user mobility scenarios. The quantitative findings align with existing literature, providing practical insights for the deployment of MIMO antennas. The study addresses limitations and proposes avenues for future research, enhancing our understanding of MIMO antenna applications and contributing to the evolution of wireless communication technologies in dynamic environments.

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region for Robust Adaptive Beamforming

In this work, a simple and effective robust adaptive beamforming technique is proposed for uniform linear arrays, which is based on the power spectral estimation and uncertainty region (PSEUR) of the interference plus noise (IPN) components. In particular, two algorithms are presented to find the angular sector of interference in every snapshot based on the adopted spatial uncertainty region of the interference direction. Moreover, a power spectrum is introduced based on the estimation of the power of interference and noise components, which allows the development of a robust approach to IPN covariance matrix reconstruction. The proposed method has two main advantages. First, an angular region that contains the interference direction is updated based on the statistics of the array data. Secondly, the proposed IPN-PSEUR method avoids estimating the power spectrum of the whole range of possible directions of the interference sector. Simulation results show that the performance of the proposed IPN-PSEUR beamformer is almost always close to the optimal value across a wide range of signal-to-noise ratios.

Performance dependency of LSTM and NAR beamformers with respect to sensor array properties in millimeter‐wave V2I scenario

AbstractInterference prediction is a challenging problem in millimeter‐wave V2I scenarios. The implementation of a practical V2I network is limited because of the interference due to the random nature of the wireless channel. This paper proposes an adaptive beamforming technique for mitigation of interference in V2I networks. In this work, long short‐term memory (LSTM) and nonlinear autoregressive (NAR)‐based regressors have been employed to predict the angles between the RSU and UE. Advance prediction of transmit and receive signals enables reliable V2I communication. Instead of predicting the beamforming matrix directly, we predict the main features using LSTM for learning dependencies in the input time series where complex variables were taken as input states and the final beamformed signal was the output. Simulation results have confirmed that the proposed LSTM model achieves comparable performance in terms of system throughput compared to the NAR method implemented as an artificial neural network.

A Robust Beamforming Suppressing Deceptive Jamming Method Based On FDA-MIMO Radar

In comparison to phased array (PA) radar that just transmits a beampattern with angle-dependent, frequency diverse array (FDA) radar with adding a feature of distance dimension performs well in main-beam deceptive jamming suppression domain. However, in practical conditions, estimating an inaccuracy of the covariance matrix(CM) has a significant negative impact on algorithm performance, also as a mismatch of target SV has. Hence, a robust adaptive beamforming technique comes up in this article that selects the principal eigenvector to get accurate signal steering vectors (SV), further gets the reconstruction interference-plus-noise CM (IPNCM). According to experiments, the proposed algorithm keeps a robust and good performance under non-ideal circumstances.

Effective Dereverberation with a Lower Complexity at Presence of the Noise

Adaptive beamforming and deconvolution techniques have shown effectiveness for reducing noise and reverberation. The minimum variance distortionless response (MVDR) beamformer is the most widely used for adaptive beamforming, whereas multichannel linear prediction (MCLP) is an excellent approach for the deconvolution. How to solve the problem where the noise and reverberation occur together is a challenging task. In this paper, the MVDR beamformer and MCLP are effectively combined for noise reduction and dereverberation. Especially, the MCLP coefficients are estimated by the Kalman filter and the MVDR filter based on the complex Gaussian mixture model (CGMM) is used to enhance the speech corrupted by the reverberation with the noise and to estimate the power spectral density (PSD) of the target speech required by the Kalman filter, respectively. The final enhanced speech is obtained by the Kalman filter. Furthermore, a complexity reduction method with respect to the Kalman filter is also proposed based on the Kronecker product. Compared to two advanced algorithms, the integrated sidelobe cancellation and linear prediction (ISCLP) method and the weighted prediction error (WPE) method, which are very effective for removing reverberation, the proposed algorithm shows better performance and lower complexity.

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

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.

Adaptive beamforming based on minimum variance (ABF-MV) using deep neural network for ultrafast ultrasound imaging

Ultrafast ultrasound imaging can achieve high frame rate by emitting planewave (PW). However, the image quality is drastically degraded in comparison with traditional scanline focused imaging. Using adaptive beamforming techniques can improve image quality at cost of real-time performance. In this work, an adaptive beamforming based on minimum variance (ABF-MV) with deep neural network (DNN) is proposed to improve the image performance and to speed up the beamforming process of ultrafast ultrasound imaging. In particular, a DNN, with a combination architecture of fully-connected network (FCN) and convolutional autoencoder (CAE), is trained with channel radio-frequency (RF) data as input while minimum variance (MV) beamformed data as ground truth. Conventional delay-and-sum (DAS) beamformer and MV beamformer are utilized for comparison to evaluate the performance of the proposed method with simulations, phantom experiments, and in-vivo experiments. The results show that the proposed method can achieve superior resolution and contrast performance, compared with DAS. Moreover, it is remarkable that both in theoretical analysis and implementation, our proposed method has comparable image quality, lower computational complexity, and faster frame rate, compared with MV. In conclusion, the proposed method has the potential to be deployed in ultrafast ultrasound imaging systems in terms of imaging performance and processing time.

Mainlobe maintenance for FDA-MIMO radar

During radar detection, the adaptive beamforming technique needs to create nulls to resist the interference. When the interference appears within the mainlobe, the main detection beam will become distorted. To solve this problem, a new method for the covariance matrix reconstruction is proposed for the mainlobe maintenance problem, and a worst-case performance optimization (WCPO) algorithm with enhanced waveform robustness is obtained. Its integration is performed over a known region without the target information to obtain the improved covariance matrix, which is then brought into the convex optimization equation to solve for the weight vector. Simulation results show that the output signal-to-noise ratio of this algorithm is higher than that of the conventional WCPO algorithm with a robust beam for the same signal-to-noise ratio. It can also effectively eliminate the primary lobe offset and secondary lobe elevation problems.

Robust adaptive beamforming based on covariance matrix reconstruction with RCB principle

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.

Adaptive orientational beamforming techniques for narrowband interference rejection

In this paper, the adaptive theory of the orientational beamforming (OBF) system is investigated. Three adaptive OBF algorithms, namely the orientational maximum signal-to-interference-plus-noise ratio (O-MSINR), the orientational linearly constrained minimum variance (O-LCMV), and the orientational generalized sidelobe canceller (O-GSC) algorithms, are proposed to reject narrowband interferences (NBIs) and recover the desired ultra-wideband (UWB) signal in the OBF system. They are developed by applying adaptive techniques in conventional directional beamforming systems to the OBF system, taking into account the similarities and differences between the OBF system and the conventional beamforming system. These algorithms are constructed on the orientation dimension, and orientational constraints instead of directional constraints are used to guarantee an orientational gain. Therefore, they are independent of signal directions. This perfectly solves the problem when an NBI has the same direction as the desired UWB signal, which current adaptive beamforming algorithms cannot handle. If all the NBIs have the same center frequency, the three algorithms work effectively regardless of the number of NBIs. If all the NBIs have different center frequencies, the O-LCMV and O-GSC algorithms outperform the O-MSINR algorithm. Simulation results show that the proposed algorithms significantly reduce the bit error rates as compared with the original OBF system.

Joint Transmitting Subarray Partition and Beamforming for Active Jamming Suppression in Phased‐MIMO Radar

AbstractAs the tradeoff between conventional phased‐array and multiple‐input multiple‐output (MIMO) radars, phased‐MIMO radar has been studied in recent years, which can simultaneously obtain coherent processing gain and waveform diversity gain. In this paper, in order to effectively suppress the jamming signal and improve the target detection performance of radar system, a joint transmitting subarray partition and beamforming method in phased‐MIMO radar is proposed. To be specific, at the transmitter, the transmitting array of radar system is partitioned into a certain number of non‐overlapped subarrays, and the subarray structure of transmitting array and the corresponding beamformer weight vectors are simultaneously optimized by constructing the optimization model of maximizing the signal‐to‐noise ratio of the whole received echoes under certain constraint conditions, so as to effectively maximize the power utilization of radar transmitter, reduce the peak sidelobe level of transmit beampattern and improve the radar anti‐jamming performance. Moreover, the adaptive beamforming technique is applied to suppress the jamming signal entering the receiver, so that the transmitting and receiving adaptive degrees of freedom are jointly utilized to enhance the anti‐jamming capability of radar system. Simulation results demonstrate the validity of the proposed method.

Reverberation Clutter Suppression Using 2-D Spatial Coherence Analysis.

Diffuse reverberation clutter often significantly degrades the visibility of abdominal structures. Reverberation clutter acts as a temporally stationary haze that originates from the multiple scattering within the subcutaneous layers and has a narrow spatial correlation length. We recently presented an adaptive beamforming technique, Lag-one Spatial Coherence Adaptive Normalization (LoSCAN), which can recover the contrast suppressed by incoherent noise. LoSCAN successfully suppressed reverberation clutter in numerous clinical examples. However, reverberation clutter is a 3-D phenomenon and can often exhibit a finite partial correlation between receive channels. Due to a strict noise-incoherence assumption, LoSCAN does not eliminate correlated reverberation clutter. This work presents a 2-D matrix array-based LoSCAN method and evaluates matrix-LoSCAN-based strategies to suppress partially correlated reverberation clutter. We validated the proposed matrix LoSCAN method using Field II simulations of a 64×64 symmetric 2-D array. We show that a subaperture beamforming (SAB) method tuned to the direction of noise correlation is an effective method to enhance LoSCAN's performance. We evaluated the efficacy of the proposed methods using fundamental and harmonic channel data acquired from the liver of two healthy volunteers using a 64×16 custom 2-D array. Compared to azimuthal LoSCAN, the proposed approach increased the contrast by up to 5.5 dB and the generalized contrast-to-noise ratio (gCNR) by up to 0.07. We also present analytic models to understand the impact of partially correlated reverberation clutter on LoSCAN images and explain the proposed methods' mechanism of image quality improvement.

Multi-target vital signs detection using frequency-modulated continuous wave radar

Respiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.

Multiple interferences suppression with space-polarization null-decoupling for polarimetrie array

The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain. When there is the interference in mainlobe, it will cause serious mainlobe distortion, that the target detection suffers from. To overcome this problem and make radar cope with the complex multiple interferences scenarios, we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar. Specifically, the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization (DoP), and a filtering criterion based on the proposed linear constraint. The signal preprocessing provides the accurate estimated parameters of the interference, which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe. The proposed method can reduce the main lobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences. The effectiveness of the proposed method is verified by simulations.

Multitarget Respiration Detection With Adaptive Digital Beamforming Technique Based on SIMO Radar

The Doppler radar has been widely used in respiration detection. However, most of the existing microwave respiration detection works are intended for either a single human subject in front of the radar or multiple subjects with known positions. In this article, a system based on single-input–multiple-output (SIMO) continuous-wave (CW) radar equipped with adaptive digital beamforming (ADBF) technique is presented to detect the respiration of multiple human subjects at unknown positions simultaneously. A solution based on the modified Capon (m-Capon) direction-of-arrival (DOA) estimation and linear constraint minimal variance (LCMV) ADBF is proposed to automatically find the angles of human subjects. By forming spatially distributed beams toward the subjects of interest, human respiration can be remotely obtained. Furthermore, for the detection of each person’s respiration, nulls are also generated at the angles of nearby interfering subjects, which results in high target discrimination capability when multiple human subjects are close to each other. The experimental results show good detection accuracy compared with the reference sensor, which verifies the effectiveness of the developed system for multitarget respiration detection.

An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors

In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample covariance matrix (DSCM), in which the auxiliary sensors are selected to decouple the array. The decoupled interference-plus-noise covariance matrix (DINCM) is reconstructed with the estimated interference SV and maximum eigenvalue of the DSCM. Furthermore, the desired signal SV is estimated as the corresponding eigenvector determined by the correlation coefficients of the assumed SV and eigenvectors. Finally, the optimal weighting vector is obtained by combining the reconstructed DINCM and the estimated desired signal SV. Our simulation results show significant signal-to-interference-plus-noise ratio (SINR) enhancement of the proposed method over existing methods under multiple types of array errors.