2010IEEE Transactions on Aerospace and Electronic SystemsRequires access

Fully Automatic Computation of Diagonal Loading Levels for Robust Adaptive Beamforming

Lin Du, Jian Li, Petre Stoica

Open publisher page 306 citations

Abstract

The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.

About this research paper

What this paper is about

The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.

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OpenAlex reports 306 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.

Key concepts: Capon, Covariance matrix, Diagonal, Adaptive beamformer, Algorithm, Computation, Beamforming, Computer science

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