Traditional Adaptive Beamforming Research Articles

A novel adaptive beamforming scheme for array signal data processing

When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal cancelation phenomenon, resulting in a sharp decline in performance. To address the occurrence of desired signal cancelation, an improved matrix projection-based efficient beamforming method is proposed. Firstly, based on spatial partitioning (SP) technology, a significant projection matrix for interference-plus-noise space (INS) is constructed. Secondly, using the constructed key projection matrix, the sample data covariance matrix is projected into the INS to achieve the goal of suppressing the desired signal data information. Finally, the weight data vector is calculated by Capon beamformer. The proposed algorithm does not require an iterative search for the optimal solution, which has the advantage of a small amount of calculation. Simulation experiments have verified that the proposed method has significant advantages in suppressing the desired data signals. Especially when the desired data signal has large power, the signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm is better than that of the compared algorithms under the conditions of random directionality errors or local scattering errors between the desired signal and interference.

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.

Robust Block Sparse Beamforming for Strictly Non-Circular Signals and Unknown Array Mutual Coupling

Widely linear beamformers (WLBs) can greatly outperform the traditional adaptive beamformers. However, the performance of WLBs is extremely susceptible to the unknown mutual coupling (MC) effects caused by the imperfectly calibrated antennas array. In this brief, we propose a block sparse representation scheme and design a robust WLB for strictly non-circular (SNC) signals to mitigate the unknown MC mismatch. We use the structural characteristics of the steering vector under MC, and derive a new augmented formulation for the observed signals which takes into account the MC mismatches. We solve a block sparse optimization problem in order to estimate the directions of illuminating signals allowing us to reconstruct the augmented interference-plus-noise covariance matrix (AINCM). We then propose to estimate the augmented desired signal steering vector using the augmented subspace projection method. Our proposed robust WLB not only avoids estimating the MC coefficients, but also prevents the signal cancellation phenomenon from reconstructing the AINCM. Our simulation results demonstrate that the proposed WLB is effective for the SNC signals, outperforms the existing methods, and is robust against unknown MC and look direction mismatches.

A coherent signal beamforming technique based on sub-array cross correlation

In this paper, a new coherent signal beamforming technique based on sub-array cross correlation (SCC) is proposed for the signal cancellation caused by traditional adaptive beamforming in multipath environment. Firstly, the array is uniformly divided into two mutual non-overlapped sub-arrays, and the preprocess of blocking expected signal is performed inside every sub-array to remove the expected signal component from the received signal on the premise that the direction of expected signal is known in advance. Then, minimum variance distortionless response (MVDR) allows us to obtain the desired signal with noise. Afterwards, based on the fact that the noise between different elements is not correlated, the MVDR output of one sub-array can be taken as the expected signal of the other sub-array to perform the beamforming with minimum mean square error (MMSE) criterion. Finally, the weight for the full array is obtained by combining the MMSE beamforming weights of the two sub-arrays. The proposed method can effectively solve the above coherent beamforming problem. What's more, the SCC beamformer performs well in the case with small samples. Both theoretical derivation and simulation experiment verify the feasibility and validity of the proposed beamformer.

Space‐time matched filter design for interference suppression in coherent frequency diverse array

By transmitting a single frequency-shifted waveform, coherent frequency diverse array (FDA) can provide a simple way to realize full spatial coverages with stable gains. Owing to the range-angle dependency in coherent FDA, the authors implement a two-dimensional angle-time matched filter to perform equivalent transmit beamforming and matched filtering simultaneously. However, such filter structures merely control main-lobes of equivalent transmit beams towards target directions. It fails to form nulls at interference directions. Moreover, traditional adaptive beamforming by designing adaptive weight vectors are no longer applicable. Additionally, they find that the range resolution scales linearly with the element number. To tackle these issues, a space-time matched filter (STMF) in combination with the hybrid coding technique is proposed. Aiming at mitigating interferences, the STMF is designed with a formulation of quadratically constrained quadratic programing. By relaxation of quadratic constraints, the hard non-convex problem can be turned into the second-order cone programing to obtain optimal filter parameters. Furthermore, the hybrid coding technique is devised to jointly improve the range resolution. Numerical experiments of both two-dimensional range-angle outputs and one-dimensional range profiles via filtering are provided, which demonstrate that the STMF with hybrid coding can effectively suppress sidelobe interferences with a range resolution enhancement.

Adaptive and hybrid Kronecker product beamforming for far-field speech signals

This work presents a Kronecker product based methodology of frequency-domain beamforming of large sensor arrays for far-field broadband speech signals. The principal idea involves splitting up a given uniform linear array (ULA) into two smaller virtual ULAs (VULAs), using the Kronecker product. The linear system of the original ULA is bifurcated into two smaller linear systems of the VULAs. Henceforth, traditional adaptive beamformers such as the minimum-variance-distortionless-response (MVDR) beamformer may be obtained for each of the VULAs, using lesser data to estimate the statistics. The short-length beamformers, obtained from the VULAs, are finally combined by the Kronecker product to derive the full-length Kronecker product beamformer. Additionally, the VULAs allow fixed and adaptive beamforming to be implemented separately on each of them. As fixed beamformers do not employ statistical information, the Kronecker product hybrid beamformers reduce the original linear system to just a small linear system involving one VULA. Accordingly, hybrid beamformers may be implemented using traditional fixed beamformers, such as the delay-and-sum (DS) beamformer, on one VULA, and traditional adaptive beamformers, such as the MVDR, on the other. The proposed Kronecker product beamformers are observed to provide faster convergence and superior robustness with respect to the traditional beamformers.

An improved robust adaptive beamforming based on worst-case performance optimization

Generally, the adaptive beamformer has better spatial resolution and much better interference rejection capability than the conventional data-independent beamformer. But, in practice, the performance of the traditional adaptive beamformer will degrade greatly if some assumptions or information on the propagation model, array parameters, and signal model are imprecise. In this paper, a new approach to robust adaptive beamforming based on optimization of worst-case performance by parameter auto-adjust (PAWCO) is proposed, which can estimate the power of the signal using Newton-Raphson interactions. The method can confirm the upper bound of the mismatch vector neither overly nor under estimated. Theoretical analysis and simulation results are presented to show the proposed beamformer can estimate the power of signal precisely while achieving better spatial resolution. The robustness of the PAWCO algorithm to environment mismatch is also demonstrated by the acoustical experimental data.

Forward-backward generalized sidelobe canceler beamforming applied to medical ultrasound imaging

For adaptive ultrasound imaging, accurate estimation of the covariance matrix is of great importance, and it has a fundamental influence on the performance of the adaptive beamformer. In this paper, a new forward-backward generalized sidelobe canceler (FBGSC) approach is proposed for medical ultrasound imaging, which uses forward and backward subaperture averaging to accurate estimate the covariance matrix. And resulted from accurate estimating of covariance matrix, FBGSC can achieve better lateral resolution and contrast without preprocessing algorithms. Field II is applied to obtain the simulated echo data of scattering points and a circular cyst. Beamforming responses of scattering points show that FBGSC can improve the lateral resolution by 55.7% and 66.6% compared with synthetic aperture (SA) and delay-and-sum (DS), respectively. Similarly, the simulated results of circular cyst show that FBGSC can obtain better beamforming responses than traditional adaptive beamformers. Finally, an experiment is conducted based on the real echo data of a medical ultrasound system. Results demonstrate that the FBGSC can improve the imaging quality of medical ultrasound imaging system, with lower computational demand and higher reliability.

Plane wave compounding based on a joint transmitting-receiving adaptive beamformer.

Plane wave compounding is a useful mode for ultrasound imaging because it can make a good compromise between imaging quality and frame rate. It is also useful for broad view ultrasound imaging. Traditional coherent plane wave compounding coherently sums the echo data of different steered transmitting waves as the output. The data correlation information of different emissions is not considered. Therefore, some adaptive techniques can be introduced into the compounding procedure. In this paper, we propose a Joint Transmitting-Receiving (JTR) adaptive beamforming scheme for plane wave compounding. Unlike traditional adaptive beamformers, the proposed beamforming scheme is designed for the 2-D data set obtained from multiple plane wave firings. It calculates both the transmitting aperture weights and the receiving aperture weights and then combines them into a 2-D adaptive weight function for compounding. Experiments are conducted on both simulated and phantom data. Results show that the proposed scheme has better performance on both point targets and cysts than the existing plane wave compounding approach. Because of the adaptive process in both apertures for compounding, an improved resolution is observed in both simulation and phantom studies. When the eigenanalysis is introduced, a contrast enhancement is achieved. For the simulated cyst, a contrast ratio (CR) improvement of 48% is achieved compared with the traditional plane wave compounding. For the phantom cyst, this improvement is 213.8%. The proposed scheme also has good robustness against sound velocity errors. Therefore, it is effective in enhancing the coherent plane wave compounding quality.

Adaptive multiple‐input multiple‐output radar beamforming based on direct data domain approach

An adaptive multiple-input multiple-output (MIMO) radar beamforming technique based on direct data domain (D3) approach is presented. The proposed method generally outperforms traditional adaptive beamformers in surprise or rapidly changing interference scenarios due to the use of a D3 interference cancellation technique, which utilises a single snapshot for adaptive beamforming. The proposed D3 approach suffers from performance degradation in the presence of uncertainty in the knowledge of target direction. To overcome this problem, a robust D3 approach using worst-case performance optimisation is developed to achieve robustness against look direction error.

Reconfigurable Adaptive Array Beamforming by Antenna Selection

Traditional adaptive array beamforming with a fixed array configuration can lead to significant inefficiencies and performance loss under different scenarios. As antennas become smaller and cheaper relative to front-ends, it becomes important to devise a reconfigurable adaptive antenna array (RAAA) strategy to yield high signal to noise and interference ratio using fewer antennas. This is achieved by selecting K from N antennas to minimize the Spatial Correlation Coefficient (SCC) between the desired signal and the interference. The lower bound of optimum SCC is formulated with two relaxation methods to give information about the suitable number of selected antennas K. A Correlation Measurement (CM) method is proposed to select the optimum subarray with K antennas, thereby reducing complexity. We carry out performance analysis and show that a 1/K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -suboptimum solution can be guaranteed with arbitrary shaped arrays. Furthermore, a Difference of Convex Sets (DCS) method is proposed to select the optimum subarray with controlled quiescent pattern in order to reduce the effect of interference during the reconfiguration time. The utility of the proposed array reconfiguration for performance improvement without increasing the cost is demonstrated using both simulated and experimental data.

Hierarchical beamformer and cross-talk reduction in electroneurography

Electroneurography (ENG) is a method of recording neural activity within nerves. Using nerve electrodes with multiple contacts the activation patterns of individual neuronal fascicles can be estimated by measuring the surface voltages induced by the intraneural activity. The information about neuronal activation can be used for functional electric stimulation (FES) of patients suffering from spinal chord injury, or to control a robotic prosthetic limb of an amputee. However, the ENG signal estimation is a severely ill-posed inverse problem due to uncertainties in the model, low resolution due to limitations of the data, geometric constraints and the difficulty in separating the signal from biological and exogenous noise. In this paper, a reduced computational model for the forward problem is proposed, and the ENG problem is addressed by using beamformer techniques. Furthermore, we show that using a hierarchical statistical model, it is possible to develop an adaptive beamformer algorithm that estimates directly the source variances rather than the voltage source itself. The advantage of this new algorithm, e.g., over a traditional adaptive beamformer algorithm, is that it allows a very stable noise reduction by averaging over a time window. In addition, a new projection technique for separating sources and reducing cross-talk between different fascicle signals is proposed. The algorithms are tested on a computer model of realistic nerve geometry and time series signals.

Spatial Virtual Polarization Filter Design for Radar Sensor

As interferences are introduced from main-lobe direction which results in tar-get signal are masked by interference, traditional adaptive beam-forming can not suppress main-lobe interferences effectively. Polarization filtering is a main method to suppress the interference in radar and other sensor systems. Based on the spatial polarization characteristic (SPC) of antenna, a spatial virtual polarization filter (SVPF) is proposed by constructing polarization decomposition and polarization estimation of the received signal. A generalized construction method of SVPF is provided, the performance of the SVPF is given, and simulation results of the interference suppressing are illustrated. It is indicated that the proposed filter is a valid filtering technique for interference canceling. SVPF made single polarized radar own polarization information processing ability which improved radar working performance.

Hermetic array processing for underwater echo‐ranging systems.

Hermetic array processing provides enhanced beam width resolution, as well as correspondingly greater gain against ambient noise and reverberation, without the requirement of data‐adaptive processing. The technique derives from the discrete hermetic (spectral) transform, and has been applied to an underwater echo‐ranging system deployed in an underwater security system. Theoretical and empirical results are also described, and the impact on sonar system performance is presented. A description of practical implementation issues and a comparison to traditional adaptive beamforming are shown.

ROBUST ADAPTIVE BEAMFORMING BASED ON COVARIANCE MATRIX RECONSTRUCTION FOR LOOK DIRECTION MISMATCH

The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch between the actual and presumed steering vectors can be mainly due to the error in the look direction estimate. In this paper, we propose a novel algorithm to estimate the look direction and to reconstruct the covariance matrix so that near optimal performance without the efiect of saturation can be achieved as the input SNR increases. Numerical results also show that all existing beamforming algorithms sufier from saturation efiect as the input SNR increases.

Efficient implementation of robust adaptive beamforming based on worst-case performance optimisation

Traditional adaptive beamforming methods undergo serious performance degradation when a mismatch between the presumed and the actual array responses to the desired source occurs. Such a mismatch can be caused by desired look direction errors, distortion of antenna shape, scattering due to multipath, signal fading as well as other errors. This mismatch entails robust design of the adaptive beamforming methods. Here, the robust minimum variance distortionless response (MVDR) beamforming based on worst-case (WC) performance optimisation is efficiently implemented using a novel ad hoc adaptive technique. A new efficient implementation of the robust MVDR beamformer with a single WC constraint is developed. Additionally, the WC optimisation formulation is generalised to include multiple WC constraints which engender a robust linearly constrained minimum variance (LCMV) beamformer with multiple-beam WC (MBWC) constraints. Moreover, the developed LCMV beamformer with MBWC constraints is converted to a system of nonlinear equations and is efficiently solved using a Newton-like method. The first proposed implementation requires low computational complexity compared with the existing techniques. Furthermore, the weight vectors of the two developed adaptive beamformers are iteratively updated using iterative gradient minimisation algorithms which eliminate the estimation of the sample matrix inversion. Several scenarios including angle-of-incidence mismatch and multipath scattering with small and large angular spreads are simulated to study the robustness of the developed algorithms.

Effect on surface ship interference on adaptive beamforming for the shallow water array performance (SWAP) project

The shallow water array performance (SWAP) project is designed to explore the limits of large-aperture passive sonar array processing capability in a shallow-water environment with moving surface ship interference. The array, off the eastern coast of Florida near Ft. Lauderdale, has 500 elements with a total length of approximately 900 m. One challenge for the processing is the length of observation time needed by traditional adaptive beamforming formulations—with an array of this size and element quantity, the resolution of range and bearing cells is such that a ship may move across many cells during the snapshot time, spreading the resulting eigenvector structure and decreasing effective signal gain. A central question is the trade-off between array gain and this eigenvector spreading loss. To explore this question, tracks from actual ships in the vicinity of the array site, combined with sound speed and bathymetry data from the site, are used in an adiabatic normal mode simulation to predict the acoustic response across the array. The resulting simulated snapshots are used in adaptive and non-adaptive formulations to predict target detection performance as a function of the interference environment and processing parameters, for both full and sub-aperture processing schemes.

Robust adaptive beamforming for general-rank signal models

The performance of adaptive beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer data snapshots because in this case, the adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust adaptive beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust adaptive beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional adaptive beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.

Experimental performance of adaptive beamforming in a sonar environment with a towed array and moving interfering sources

The performances of adaptive array algorithms are known to suffer from a strong degradation in scenarios with moving interfering sources. In this article, basic adaptive beamforming techniques are compared using shallow sea sonar data recorded in a towed horizontal array environment with moving interfering sources originated from shipping noise. Our experimental results show the relationship between the practical performances of adaptive and conventional beamforming techniques compared in terms of output SINR or a related measure given by the noncompensated postbeamforming interference power. These results demonstrate noticeable performance improvements that can be achieved using several robust algorithms relative to traditional adaptive beamforming schemes.

5 - Antibacterial Therapy — Special Considerations in Neutropenic Patients

This work presents a Kronecker product based methodology of frequency-domain beamforming of large sensor arrays for far-field broadband speech signals. The principal idea involves splitting up a given uniform linear array (ULA) into two smaller virtual ULAs (VULAs), using the Kronecker product. The linear system of the original ULA is bifurcated into two smaller linear systems of the VULAs. Henceforth, traditional adaptive beamformers such as the minimum-variance-distortionless-response (MVDR) beamformer may be obtained for each of the VULAs, using lesser data to estimate the statistics. The short-length beamformers, obtained from the VULAs, are finally combined by the Kronecker product to derive the full-length Kronecker product beamformer. Additionally, the VULAs allow fixed and adaptive beamforming to be implemented separately on each of them. As fixed beamformers do not employ statistical information, the Kronecker product hybrid beamformers reduce the original linear system to just a small linear system involving one VULA. Accordingly, hybrid beamformers may be implemented using traditional fixed beamformers, such as the delay-and-sum (DS) beamformer, on one VULA, and traditional adaptive beamformers, such as the MVDR, on the other. The proposed Kronecker product beamformers are observed to provide faster convergence and superior robustness with respect to the traditional beamformers.