Adaptive beamformer

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

Method for noise suppression in an adaptive beamformer

Abstract: A method for noise suppression is described, wherein noisy input signals in a multiple input audio processing device are subjected to adaptations and summed and wherein the noise frequency components of the noisy input signals in the summed input signals are estimated based on individually kept noise frequency components and on said adaptations. Advantageously the method may be applied if a spectral subtraction like technique is applied in a multi input beamformer. Only one spectral frequency transformation is necessary, which reduces the number of necessary calculations.

Robust Adaptive Beamforming Using Multidimensional Covariance Fitting

Abstract: Over the last decade, several set-based worst-case beamformers have been proposed. It has been shown that some of these beamformers can be formulated equivalently as one-dimensional (ID) covariance fitting problems. Based on this formulation, we show that these beamformers lead to inherently nonoptimum results in the presence of interferers. To mitigate the detrimental effect of interferers, we extend the ID covariance fitting approach to multidimensional (MD) covariance fitting, modeling the source steering vectors by means of uncertainty sets. The proposed MD covariance fitting approach leads to a nonconvex optimization problem. We develop a convex approximation of this problem, which can be solved, for example, by means of the logarithmic barrier method. The complexity required to compute the barrier function and its first- and second-order derivatives is derived. Simulation results show that the proposed beamformer based on MD covariance fitting achieves an improved performance as compared to the state-of-the-art narrowband beamformers in scenarios with large sample support.

Utilizing Waveform Features for Adaptive Beamforming and Direction Finding with Narrowband Signals

Abstract: ■ Extensive research has been done on the use of antenna arrays for direction finding and beamforming; this research focuses on the detailed behavior of specific techniques rather than on actual signal processing applications. In most applications, there is a fundamental signal feature that provides essential leverage for an effective processing approach. This article, which is structured around such features, presents a comprehensive framework for selecting an appropriate adaptive approach for processing cochannel narrowband signals. We address the roles of antenna calibration and prior waveform knowledge, and give examples of effective, practical direction-finding and beamforming procedures that cover a wide range of potential applications.

An analysis of the projection method for robust adaptive beamforming

Abstract: Two causes of performance degradation in adaptive beamforming are perturbation error and sample covariance error These causes were discussed by Feldman and Griffihs (see IEEE Trans Signal Processing, vol42, p867, April, 1994), which proposed an approach termed the projection method to reduce the sensitivity of the beamformer performance to both types of error This paper contains an analysis of the expected performance of the beamformer when it is implemented, either with or without using the projection method Based on a second-order Taylor series expansion of an approximate expression for the signal to interference-plus-noise ratio (SINR) at the beamformer output, the analysis provides accurate results when either perturbation error or sample covariance error occurs A less accurate analysis based on a zeroth-order Taylor series expansion includes results presented by Jablon (1986) and Chang and Yeh (see ibid, vol40, no11, p1336, 1992) as special cases

On low rank MVDR beamforming using the conjugate gradient algorithm

Abstract: In array applications an important task is adaptive beamforming. The minimum variance distortionless response (MVDR) beamformer can only be computed if the true spatial correlation matrix is available. In practice, the correlation matrix has to be estimated from the arriving signals, and in some cases there are only a small number of samples (snapshots) available. When the number of snapshots is small, the MVDR beamformer is no longer optimal, and a low rank MVDR solution can provide a higher SINR. In this work we analyze two methods of finding low rank solutions: steering independent conjugate gradient (SI-CG) and steering dependent conjugate gradient (SD-CG). We also propose a simplified expression to compute the arriving power from any given direction.