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Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.


Papers
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Journal ArticleDOI
TL;DR: A beamforming structure is presented which can be used to implement a wide variety of linearly constrained adaptive array processors and is shown to incorporate algorithms which have been suggested previously for use in adaptive beamforming as well as to include new approaches.
Abstract: A beamforming structure is presented which can be used to implement a wide variety of linearly constrained adaptive array processors. The structure is designed for use with arrays which have been time-delay steered such that the desired signal of interest appears approximately in phase at the steered outputs. One major advantage of the new structure is the constraints can be implemented using simple hardware differencing amplifiers. The structure is shown to incorporate algorithms which have been suggested previously for use in adaptive beamforming as well as to include new approaches. It is also particularly useful for studying the effects of steering errors on array performance. Numerical examples illustrating the performance of the structure are presented.

1,923 citations

Journal ArticleDOI
TL;DR: It is shown that a simple scaling of the projection of tentative weights, in the subspace orthogonal to the linear constraints, can be used to satisfy the quadratic inequality constraint.
Abstract: Adaptive beamforming algorithms can be extremely sensitive to slight errors in array characteristics. Errors which are uncorrelated from sensor to sensor pass through the beamformer like uncorrelated or spatially white noise. Hence, gain against white noise is a measure of robustness. A new algorithm is presented which includes a quadratic inequality constraint on the array gain against uncorrelated noise, while minimizing output power subject to multiple linear equality constraints. It is shown that a simple scaling of the projection of tentative weights, in the subspace orthogonal to the linear constraints, can be used to satisfy the quadratic inequality constraint. Moreover, this scaling is equivalent to a projection onto the quadratic constraint boundary so that the usual favorable properties of projection algorithms apply. This leads to a simple, effective, robust adaptive beamforming algorithm in which all constraints are satisfied exactly at each step and roundoff errors do not accumulate. The algorithm is then extended to the case of a more general quadratic constraint.

1,851 citations

Journal ArticleDOI
TL;DR: A new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch is developed based on the optimization of worst-case performance.
Abstract: Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is present in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors (spatial signatures). Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration, distorted antenna shape, as well as distortions caused by medium inhomogeneities, near-far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance. It turns out that the natural formulation of this adaptive beamforming problem involves minimization of a quadratic function subject to infinitely many nonconvex quadratic constraints. We show that this (originally intractable) problem can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently (in polynomial time) using the well-established interior point method. It is also shown that the proposed technique can be interpreted in terms of diagonal loading where the optimal value of the diagonal loading factor is computed based on the known level of uncertainty of the signal steering vector. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms.

1,347 citations

Book
01 Jan 2001
TL;DR: This paper presents a meta-modelling architecture for microphone Array Processing that automates the very labor-intensive and therefore time-heavy and expensive process of manually shaping Microphone Arrays for Speech Input in Automobiles.
Abstract: I. Speech Enhancement.- 1 Constant Directivity Beamforming.- 2 Superdirective Microphone Arrays.- 3 Post-Filtering Techniques.- 4 Spatial Coherence Functions for Differential Microphones in Isotropic Noise Fields.- 5 Robust Adaptive Beamforming.- 6 GSVD-Based Optimal Filtering for Multi-Microphone Speech Enhancement.- 7 Explicit Speech Modeling for Microphone Array Speech Acquisition.- II. Source Localization.- 8 Robust Localization in Reverberant Rooms.- 9 Multi-Source Localization Strategies.- 10 Joint Audio-Video Signal Processing for Object Localization and Tracking.- III. Applications.- 11 Microphone-Array Hearing Aids.- 12 Small Microphone Arrays with Postfilters for Noise and Acoustic Echo Reduction.- 13 Acoustic Echo Cancellation for Beamforming Microphone Arrays.- 14 Optimal and Adaptive Microphone Arrays for Speech Input in Automobiles.- 15 Speech Recognition with Microphone Arrays.- 16 Blind Separation of Acoustic Signals.- IV. Open Problems and Future Directions.- 17 Future Directions for Microphone Arrays.- 18 Future Directions in Microphone Array Processing.

1,309 citations

Journal ArticleDOI
TL;DR: It is shown that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches, but the amount of diagonalloading can be precisely calculated based on the uncertainty set of the steering vector.
Abstract: The Capon (1969) beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of the SOI steering vector is imprecise (as is often the case in practice), the performance of the Capon beamformer may become worse than that of the standard beamformer. Diagonal loading (including its extended versions) has been a popular approach to improve the robustness of the Capon beamformer. We show that a natural extension of the Capon beamformer to the case of uncertain steering vectors also belongs to the class of diagonal loading approaches, but the amount of diagonal loading can be precisely calculated based on the uncertainty set of the steering vector. The proposed robust Capon beamformer can be efficiently computed at a comparable cost with that of the standard Capon beamformer. Its excellent performance for SOI power estimation is demonstrated via a number of numerical examples.

1,113 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202371
2022168
2021133
2020154
2019198
2018154