Showing papers on "Adaptive beamformer published in 2011"

Optimal Modal Beamforming for Spherical Microphone Arrays

Abstract: An approach to optimal array pattern synthesis based on spherical harmonics is presented. The array processing problem in the spherical harmonics domain is expressed with a matrix formulation. The beamformer weight vector design problem is written as a multiply constrained problem, so that the resulting beamformer can provide a suitable trade-off among multiple conflicting performance measures such as directivity index, robustness, array gain, sidelobe level, mainlobe width, and so on. The multiply constrained problem is formulated as a convex form of second-order cone programming which is computationally tractable. We show that the pure phase-mode spherical microphone array can be viewed as a minimum variance distortionless response (MVDR) beamformer in the spherical harmonics domain for the case of spherically isotropic noise. It is shown that our approach includes the delay-and-sum beamformer and a pure phase-mode beamformer as special cases, which leads to very flexible designs. Results of simulations and experimental data processing show good performance of the proposed array pattern synthesis approach. To simplify the analysis, the assumption of equidistant spatial sampling of the wavefield by microphones on a spherical surface is used and the aliasing effects due to noncontinuous spatial sampling are neglected.

Transmit–Receive Duplexing Using Digital Beamforming System to Cancel Self-Interference

Abstract: A near-field cancellation duplexing system is demonstrated using multiple coordinated transceivers with symmetrically arranged antenna elements and a digital backend for baseband waveform phase and amplitude weighting controls. By adapting weights of multiple transmit elements, coupled interference at receiver elements deconstructively interferes, providing up to 50 dB of additional isolation over the coupling of a single transmitter to a receiving element. Bandwidth considerations of the array are presented. It is shown that uncorrelated transmit noise from multiple transmitters can be removed through tunable filtering or through adaptive beamforming of multiple receiving elements, both providing an additional 30-40 dB of interference reduction that is tunable over the frequency range of the system.

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

Abstract: In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forwardbackward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forwardbackward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.

A Novel Adaptive Beamforming Technique Applied on Linear Antenna Arrays Using Adaptive Mutated Boolean PSO

Abstract: The present work introduces a new optimization technique suitable for adaptive beamforming of linear antenna arrays. The proposed technique is a new PSO variant called Adaptive Mutated Boolean PSO (AMBPSO) where the update formulae are implemented exclusively in Boolean form by using an e-ciently adaptive mutation process. The AMBPSO aims at estimating the excitation weights applied on the array elements considering that a desired signal and several interference signals are received by the array at respective directions of arrival. In order to exhibit the robustness of the technique, the optimization process does not take into account the interference correlation matrix. A certain power level of additive Gaussian noise is also considered by the technique. The AMBPSO has been applied in several cases of uniform linear antenna arrays with difierent spacing between adjacent elements and difierent noise power level and therefore seems to be quite promising in the smart antenna technology.

Adaptive Wideband Beamforming With Frequency Invariance Constraints

Abstract: A response variation (RV) element is introduced to control the consistency of an adaptive wideband beamformer's response over the frequency range of interest. By incorporating the RV element into the linearly constrained minimum variance (LCMV) beamformer, we develop a novel linearly constrained beamformer with an improved output signal-to-interference-plus-noise ratio (SINR), compared to both the traditional formulation and the eigenvector based formulation, due to an increased number of degrees of freedom for interference suppression. In addition, two novel wideband beamformers robust against look direction estimation errors are also proposed as a further application of the RV element. One is designed by imposing a constraint on the RV element and simultaneously limiting the magnitude response of the beamformer within a pre-defined angle range at a reference frequency; the other one is obtained by combining the RV element and the worst-case performance optimization method. Both of them are reformulated in a convex form as the second-order cone (SOC) programming problem and solved efficiently using interior point method. Compared with the original robust methods, a more efficient and effective control over the beamformer's response at the look direction region is achieved with an improved overall performance.

Microphone array system

Abstract: A method and system for enhancing a target sound signal from multiple sound signals is provided. An array of an arbitrary number of sound sensors positioned in an arbitrary configuration receives the sound signals from multiple disparate sources. The sound signals comprise the target sound signal from a target sound source, and ambient noise signals. A sound source localization unit, an adaptive beamforming unit, and a noise reduction unit are in operative communication with the array of sound sensors. The sound source localization unit estimates a spatial location of the target sound signal from the received sound signals. The adaptive beamforming unit performs adaptive beamforming by steering a directivity pattern of the array of sound sensors in a direction of the spatial location of the target sound signal, thereby enhancing the target sound signal and partially suppressing the ambient noise signals, which are further suppressed by the noise reduction unit.

Ultrasound nondestructive evaluation (NDE) imaging with transducer arrays and adaptive processing

Abstract: This paper addresses the challenging problem of ultrasonic non-destructive evaluation (NDE) imaging with adaptive transducer arrays. In NDE applications, most materials like concrete, stainless steel and carbon-reinforced composites used extensively in industries and civil engineering exhibit heterogeneous internal structure. When inspected using ultrasound, the signals from defects are significantly corrupted by the echoes form randomly distributed scatterers, even defects that are much larger than these random reflectors are difficult to detect with the conventional delay-and-sum operation. We propose to apply adaptive beamforming to the received data samples to reduce the interference and clutter noise. Beamforming is to manipulate the array beam pattern by appropriately weighting the per-element delayed data samples prior to summing them. The adaptive weights are computed from the statistical analysis of the data samples. This delay-weight-and-sum process can be explained as applying a lateral spatial filter to the signals across the probe aperture. Simulations show that the clutter noise is reduced by more than 30 dB and the lateral resolution is enhanced simultaneously when adaptive beamforming is applied. In experiments inspecting a steel block with side-drilled holes, good quantitative agreement with simulation results is demonstrated.

Uniform circular arrays for phased array antenna

Abstract: This paper compares the performances of a number of uniform circular array (UCA) configurations for phased array antennas. A UCA geometry is targeted due to its symmetrical configuration which enables the phased array antenna to scan azimuthally with minimal changes in its beam width and sidelobe levels. Each UCA configuration consists of 19 isotropic elements. Particle Swarm Optimization (PSO) is used to calculate the complex weights of the antenna array elements in order to adapt the antenna to the changing environments. Comparisons are made in the context of adaptive beamforming properties and Signal to Interference Ratio (SIR). The results obtained suggest that a planar uniform hexagonal array PUHA (1:6:12) is suitable for high resolution applications as its sidelobe levels are the lowest compared to the other geometries.

Adaptive antenna selection and Tx/Rx beamforming for large-scale MIMO systems in 60 GHz channels

Abstract: We consider a large-scale MIMO system operating in the 60 GHz band employing beamforming for high-speed data transmission. We assume that the number of RF chains is smaller than the number of antennas, which motivates the use of antenna selection to exploit the beamforming gain afforded by the large-scale antenna array. However, the system constraint that at the receiver, only a linear combination of the receive antenna outputs is available, which together with the large dimension of the MIMO system makes it challenging to devise an efficient antenna selection algorithm. By exploiting the strong line-of-sight property of the 60 GHz channels, we propose an iterative antenna selection algorithm based on discrete stochastic approximation that can quickly lock onto a near-optimal antenna subset. Moreover, given a selected antenna subset, we propose an adaptive transmit and receive beamforming algorithm based on the stochastic gradient method that makes use of a low-rate feedback channel to inform the transmitter about the selected beams. Simulation results show that both the proposed antenna selection and the adaptive beamforming techniques exhibit fast convergence and near-optimal performance.

Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch

Abstract: 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.

An integrated framework for multi-channel multi-source localization and voice activity detection

Abstract: Two of the major challenges in microphone array based adaptive beamforming, speech enhancement and distant speech recognition, are robust and accurate source localization and voice activity detection. This paper introduces a spatial gradient steered response power using the phase transform (SRPPHAT) method which is capable of localization of competing speakers in overlapping conditions. We further investigate the behavior of the SRP function and characterize theoretically a fixed point in its search space for the diffuse noise field. We call this fixed point the null position in the SRP search space. Building on this evidence, we propose a technique for multichannel voice activity detection (MVAD) based on detection of a maximum power corresponding to the null position. The gradient SRP-PHAT in tandem with the MVAD form an integrated framework of multi-source localization and voice activity detection. The experiments carried out on real data recordings show that this framework is very effective in practical applications of hands-free communication.

A Differential Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of Look Direction and Array Geometry

Abstract: The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector. The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed steering vectors can be due to the error in the position (geometry) and/or in the look direction estimate. In this paper, we propose a difierential evolution (DE) based robust adaptive beamforming that is able to achieve near optimal performance even in the presence of geometry error. Initially, we estimate an optimal steering vector by maximizing and minimizing the signal power in and out of the desired signal's angular range, respectively. Then, we estimate the look direction and reconstruct the covariance matrix. Based on the obtained steering vector, estimate for look direction and reconstructed covariance matrix, near optimal output SINR, can be obtained with the increase in the input SNR without observing any saturation even in the presence of geometry error. Numerical simulations are presented to demonstrate the e-cacy of the proposed algorithm.

Robust MVDR Beamformer for Nulling Level Control via Multi-Parametric Quadratic Programming

Abstract: MVDR beamformer is one of the well-known adaptive beamforming techniques that offers the ability to resolve signals that are separated by a fraction of an antenna beamwidth. In an ideal scenario, the MVDR beamformer can not only minimize the array output power but also maintain a distortionless mainlobe response toward the desired signal. Unfortunately, the MVDR beamformer may have unacceptably low nulling level, which may lead to significant performance degradation in the case of unexpected interfering signals. A new robust MVDR beamforming is presented to control the nulling level of adaptive antenna array. In this proposed approach, the beamforming optimization problem is formulated as a multi-parametric quadratic programming (mp-QP) problem such that the optimal weight vector can be easily obtained by real-valued computation. The presented method can guarantee that the nulling level are strictly below the prescribed threshold. Simulation results are presented to verify the efficiency of the proposed method. Received 25 February 2011, Accepted 21 March 2011, Scheduled 25 March 2011 Corresponding author: Fu Lai Liu (fulailiu@126.com). † F. L. Liu is also with School of Automation, Southeast University, Nanjing, China.

Adaptive algorithms for acoustic echo cancellation in speech processing

Abstract: Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including echo cancellation, adaptive equalization, adaptive noise cancellation, and adaptive beamforming. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to users and causes a reduction in the quality of the communication. This paper focuses on the use of LMS and NLMS algorithms to reduce this unwanted echo, thus increasing communication quality.

Robust Beamforming With Magnitude Response Constraints Using Iterative Second-Order Cone Programming

Abstract: The problem of robust beamforming for antenna arrays with arbitrary geometry and magnitude response constraints is one of considerable importance. Due to the presence of the non-convex magnitude response constraints, conventional convex optimization techniques cannot be applied directly. A new approach based on iteratively linearizing the non-convex constraints is then proposed to reformulate the non-convex problem to a series of convex subproblems, each of which can be optimally solved using second-order cone programming (SOCP). Moreover, in order to obtain a more robust beamformer against array imperfections, the proposed method is further extended by optimizing its worst-case performance using again SOCP. Different from some conventional methods which are restricted to linear arrays, the proposed method is applicable to arbitrary array geometries since the weight vector, rather than its autocorrelation sequence, is used as the variable. Simulation results show that the performance of the proposed method is comparable to the optimal solution previously proposed for uniform linear arrays, and it also gives satisfactory results under different array specifications and geometries tested.

Sensor array beamformer post-processor

Abstract: A novel beamforming post-processor technique with enhanced noise suppression capability. The present beamforming post-processor technique is a non-linear post-processing technique for sensor arrays (e.g., microphone arrays) which improves the directivity and signal separation capabilities. The technique works in so-called instantaneous direction of arrival space, estimates the probability for sound coming from a given incident angle or look-up direction and applies a time-varying, gain based, spatio-temporal filter for suppressing sounds coming from directions other than the sound source direction, resulting in minimal artifacts and musical noise.

Real-time GPU-based adaptive beamformer for high quality ultrasound imaging

Abstract: A real-time adaptive minimum variance (MV) beam- former realized using graphics processing units (GPUs) is pre- sented. MV adaptive beamforming technique is attractive as it is capable of producing high quality images with narrow mainlobe width and low sidelobe level. However, because of its substantially higher computational requirements, realizing MV in real-time has been prohibitively difficult. Recent advancements in commodity GPUs have made very high performance computing possible at very affordable price. Using a commercial off-the-shelf GPU, an MV beamformer achieving real-time performance has been realized. Tradeoffs between computational throughput and image quality have been studied. Careful selection of algorithm parameters, including receive aperture and sub-aperture size, was demonstrated to be imperative for achieving real-time performance without sacrificing image qualities.

Synthesis of Linear Antenna Array using Genetic Algorithm to Maximize Sidelobe Level Reduction

Abstract: Genetic algorithm optimization method is used in this paper for the synthesis of antenna array radiation pattern in adaptive beamforming. The synthesis problem in this paper discussed is to finding the weights of the antenna array elements that are optimum to provide the radiation pattern with maximum reduction in the sidelobe level. This technique proved its effectiveness in improving the performance of the antenna array.

Statistical Performance Prediction of Generalized Monopulse Estimation

Abstract: Monopulse is an established array processing technique for fast and accurate angle estimation. This technique has been generalized to space-time array processing of any dimension. The statistical performance of this generalized monopulse parameter estimation has been characterized for several target fluctuation models but not for all cases. This gap is filled by this paper. We derive the mean and variance of the complex and real and imaginary part of the averaged monopulse ratio for all Swerling target models, deterministic targets (Swerling 0 case), χ2 distributed targets with 4 degrees of freedom (Swerling 3 or 4 case) including a given detection threshold, for an arbitrary number of difference beams, and for an arbitrary number of noncoherent averaging of time snapshots. For completeness we give also the already known results for Rayleigh targets (Swerling 1 or 2 case) in the same notation. From these means and variances, the performance of all kinds of parameter estimates with the generalized monopulse formula can be calculated. Applications of these statistical descriptions are presented for planar arrays and adaptive beamforming and for space-time adaptive processing (STAP) for broadband interference suppression. From these examples some interesting conclusions can be drawn.

Adaptive beamforming for large arrays in satellite communications systems with dispersed coverage

Abstract: Conventional multibeam satellite communications systems ensure coverage of wide areas through multiple fixed beams where all users inside a beam share the same bandwidth. The authors consider a new and more flexible system where each user is assigned his own beam, and the users can be very geographically dispersed. This is achieved through the use of a large direct radiating array coupled with adaptive beamforming so as to reject interferences and to provide a maximal gain to the user of interest. New fast-converging adaptive beamforming algorithms are presented, which allow one to obtain good signal to interference and noise ratio with a number of snapshots much lower than the number of antennas in the array. These beamformers are evaluated on reference scenarios.

Reduced dimension robust Capon beamforming for large aperture passive sonar arrays

Abstract: In passive sonar, adaptive beamforming can be used to increase the array output signal-to-interference-plus-noise ratio (SINR) over delay-and-sum techniques, provided that array steering vector (ASV) and covariance matrix errors are accounted for. By exploiting ellipsoidal sets of the ASV, robust Capon beamformers (RCBs) systematically allow for ASV errors. For large aperture, many-element passive sonar arrays, the computational and sample support requirements often make element-space beamforming unfeasible and one is forced to consider reduced-dimension techniques. Here, a framework is proposed for combining reduced-dimension and RCB methods, producing rapidly converging, low complexity reduced-dimension RCBs (RDRCBs) allowing for ASV errors. The key contribution is the derivation of reduced-dimension ellipsoids, used by the RDRCBs, from typically available element-space sets and the dimension-reducing transformation(s) via propagation. The method allows for any ellipsoidal element-space ASV set and any full (column) rank dimension-reducing transformation. Here, for the application, the author considers the use of beamspace techniques within the RDRCB framework. The SINR of the RDRCBs are analysed, showing where they can outperform their element-space counter-parts. The benefits of using the RDRCBs are illustrated on experimental passive sonar data.

A Versatile Framework for Speaker Separation Using a Model-Based Speaker Localization Approach

Abstract: We build upon our speaker localization framework developed in a previous work (N. Madhu and R. Martin, A scalable framework for multiple speaker localization and tracking,” in Proc. Int. Workshop Acoustic Echo Noise Control (IWAENC), Sep. 2008) to perform source separation. The proposed approach, exploiting the supplementary information from the mixture of Gaussians-based localization model, allows for the incorporation of a wide class of separation algorithms, from the nonlinear time-frequency mask-based approaches to a fully adaptive beamformer in the generalized sidelobe canceller (GSC) structure. We propose, in addition, a generalized estimation of the blocking matrix based on subspace projectors. The adaptive beamformer realized as proposed is insensitive to gain mismatches among the sensors, obviating the need for magnitude calibration of the microphones. It is also demonstrated that the proposed linear approach has a performance comparable to that of an optimal (oracle) GSC implementation. In comparison to ICA-based approaches, another advantage of the separation framework described herein is its robustness to ambient noise and scenarios with an unknown number of sources.

A Novel Diagonal Loading Method for Robust Adaptive Beamforming

Abstract: The diagonal loading method is a simple and e-cient method to improve the robustness of beamformers. However, how to determine the ideal diagonal loading level has not been adequately addressed. In this paper, it is observed in the simulation that the peak of the main beam is moved with the diagonal loading level when there exists a Direction of Arrival (DOA) estimation error. Based on the observation, a novel diagonal loading method is proposed, and a tradeofi exists between the robustness and the interference suppression capability by controlling the peak location of the main beam. As long as the DOA estimation error is less than the half of the width of main beam, the proposed beamformer will not suppress the Signal of Interest (SOI) as interference. Numerical experiments prove the efiectiveness of the proposed method.

Adaptive Beamforming with Real-Valued Coefficients Based on Uniform Linear Arrays

Abstract: A class of adaptive beamforming algorithms with real-valued coefficients is proposed based on the uniform linear array structure by introducing a preprocessing transformation matrix. It is derived from the beamformer with a minimum mean square error (MSE) or a maximum output signal-to-interference-plus-noise ratio (SINR), depending on the specific design criteria. The key parameter of the transformation matrix takes different values for different beamforming scenarios and three representative examples are studied: the linearly constrained minimum variance beamformer (and the generalized sidelobe canceller), the reference signal based beamformer, and the class of blind beamformers based on the constant modulus algorithm. Its advantage is twofold: 1) with real-valued coefficients, the computational complexity of the overall system is reduced significantly; 2) a faster convergence speed is achieved and given the same stepsize, the system arrives at a lower MSE (or a higher output SINR).

Quaternion-Capon beamformer using crossed-dipole arrays

Abstract: Adaptive beamforming using a crossed-dipole array is addressed in the hypercomplex framework. A new variant of Capon beamformer, called Quaternion-Capon (Q-Capon) beamformer, is herein proposed to fulfill the linearly constrained minimization operation in the quaternion domain. Simulation results show that the Q-Capon beamformer has a better convergence performance than the standard "long-vector" Capon beamformer, and is more robust than the latter to the uncertainty in signal's steering vector especially under the scenario of high signal to noise ratio values.

Evaluation of Robust Capon Beamforming for Passive Sonar

Abstract: Adaptive beamforming is often used in passive sonar, e.g., to improve the detectability of weak sources. The recently proposed robust Capon beamformer (RCB) exploits array steering vector uncertainty sets, eliminating the need for the ad hoc parameter choices often required when implementing robust adaptive beamforming algorithms. Here, we evaluate the performance of the RCB using experimental and simulated underwater acoustics data.

Robust adaptive beamforming based on jointly estimating covariance matrix and steering vector

Abstract: In this paper, a new adaptive beamforming algorithm with joint robustness against covariance matrix uncertainty as well as steering vector mismatch is proposed. First, the theoretical covariance matrix is estimated based on the shrinkage method. Subsequently, the difference between the actual and the presumed steering vector is estimated by solving a quadratic convex optimization problem, which enables correction of the presumed steering vector. Unlike other robust beamforming techniques, neither the norm of the steering vector nor the upper bound of the norm of the mismatch vector is assumed in our approach. Simulation results show the effectiveness of the proposed algorithm both in terms of output performance and computational complexity.

Analysis of adaptive algorithms for digital beamforming in Smart Antennas

Abstract: Smart Antennas have been gaining popularity in the recent times, as a means to enhance data rate. The reason behind this development is the availability of high-end processors to handle the complex computations involved. The major advantage of digital beamformer (smart antennas) is that phase shifting and array weighing can be performed on digital data rather than in hardware. This paper analyses the performance of three adaptive algorithms — Least Mean Square (LMS), Recursive Least Square (RLS) and Conjugate Gradient Method (CGM) for computing the array weights. In this paper digital beamforming is also performed using kalman based normalised Least Mean Square algorithm. This adaptive algorithm promises very high rate of convergence, highly reduced mean square error and low computational complexity compared to the existing adaptive algorithms. The weights obtained by the above algorithm are then used to steer the antenna array beam in the direction of interest, thereby enhancing SNR. One of the major requirements for Long Term Evolution (LTE), high datarate, can hence be achieved by smart antennas.