Robust Adaptive Beamforming Technique Research Articles

A robust adaptive array beamformer using particle swarm optimization for space–time code division multiple access systems

An adaptive beamformer often suffers from severe performance degradation when a mismatch exists in the steering vector of interest. In this study, a new approach to the robust adaptive beamforming technique is proposed based on minimum variance distortionless response (MVDR) for space–time systems. The proposed method combines particle swarm optimization (PSO) and Taylor series expansion to enable the use of direction-of-arrival (DOA) estimation. Taylor series expansion can be robust to pointer errors for the presumed steering vector. However, the Taylor approach is easily trapped at a local minimum, causing errors in DOA estimation. Therefore, a more accurate DOA estimate is necessary for MVDR beamforming. To resolve this problem, a DOA estimation approach featuring a high resolution and low computational load is presented in this paper. An initial DOA estimate is first determined using a PSO estimator. Next, the predominant DOA estimate is sent to the Taylor series expansion-based estimator to form an estimate. Several simulation results are provided to demonstrate the effectiveness of the proposed approach.

Robust Adaptive Beamforming Using a Low-Complexity Shrinkage-Based Mismatch Estimation Algorithm

In this work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.

Automatic computation of diagonal loading factor for robust adaptive beamforming based on Gaussian distribution

One of the most well-known robust adaptive beamforming techniques is diagonal loading (DL). The main drawback of this approach is that the loading factor is hard to be reliably determined. Recently, the general linear combination (GLC)-based robust Capon beamformer is presented to fully automatically determine the loading factor only from the given data. In this paper, we propose an enhanced GLC-based beamforming technique based on the Gaussian assumption. It provides an analytic solution of the loading factor, which can be calculated more efficiently. The simulations show that it decreases the computational load without the degradation of performance.

Robust Adaptive Beamforming Based on Steering Vector Estimation With as Little as Possible Prior Information

A general notion of robustness for robust adaptive beamforming (RAB) problem and a unified principle for minimum variance distortionless response (MVDR) RAB techniques design are formulated. This principle is to use standard MVDR beamformer in tandem with an estimate of the desired signal steering vector found based on some imprecise prior information. Differences between various MVDR RAB techniques occur only because of the differences in the assumed prior information and the corresponding signal steering vector estimation techniques. A new MVDR RAB technique, which uses as little as possible and easy to obtain imprecise prior information, is developed. The objective for estimating the steering vector is the maximization of the beamformer output power, while the constraints are the normalization condition and the requirement that the estimate does not converge to any of the interference steering vectors and their linear combinations. The prior information used is only the imprecise knowledge of the antenna array geometry and angular sector in which the actual steering vector lies. Mathematically, the proposed MVDR RAB is expressed as the well known non-convex quadratically constrained quadratic programming problem with two constraints, which can be efficiently and exactly solved. Some new results for the corresponding optimization problem such as a new algebraic way of finding the rank-one solution from the general-rank solution of the relaxed problem and the condition under which the solution of the relaxed problem is guaranteed to be rank-one are derived. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques.

ADAPTIVE BEAMFORMING WITH LOW SIDE LOBE LEVEL USING NEURAL NETWORKS TRAINED BY MUTATED BOOLEAN PSO

A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train e-ciently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.

On Building Immersive Audio Applications Using Robust Adaptive Beamforming and Joint Audio-Video Source Localization

This paper deals with some of the different problems, strategies, and solutions of building true immersive audio systems oriented to future communication applications. The aim is to build a system where the acoustic field of a chamber is recorded using a microphone array and then is reconstructed or rendered again, in a different chamber using loudspeaker array-based techniques. Our proposal explores the possibility of using recent robust adaptive beamforming techniques for effectively estimating the original sources of the emitting room. A joint audio-video localization method needed in the estimation process as well as in the rendering engine is also presented. The estimated source signal and the source localization information drive a wave field synthesis engine that renders the acoustic field again at the receiving chamber. The system performance is tested using MUSHRA-based subjective tests.

Identification of multipole noise sources in low Mach number jets near the peak frequency

A beam-forming algorithm is developed to identify noise source structures in low Mach number jets near the peak frequency. Pressure data from a conical phased microphone array are Fourier decomposed in the azimuthal direction, and the dominant eigenvectors of a cross-spectral matrix are extracted for each azimuthal mode; subsequently, a robust adaptive beam-forming technique is applied to a cross-spectral matrix reconstructed from the primary eigenmodes. The coherent sound signals are then decomposed into multipoles in spherical coordinates, and the primary mode and its axial position are identified for the axisymmetric and first two azimuthal modes. The proposed beam-forming algorithm is applied to an unheated round jet over a range of Mach numbers and frequencies. The result indicates that at low Mach numbers, the second zenithal mode (i.e., quadrupole) appears to be dominant for each azimuthal mode, and the source position slightly shifts downstream with increasing azimuthal mode number.

Principal components, covariance matrix tapers, and the subspace leakage problem

A new class of robust space-time adaptive beamforming techniques is introduced to address a broad range of subspace leakage phenomena that arise in many sensor array applications. When present, these leakage phenomena can significantly increase the effective rank of the dominant colored noise interference spectrum, thereby reducing the appeal of techniques that exploit low-rank dominant interference (such as principal components (PC) or diagonal loading) to reduce sample support (training) requirements. By combining the covariance matrix taper (CMT) approach with either PC or diagonal loading, the minimal sample support properties of these techniques can be preserved.