A Robust LCMP Beamformer with Limited Snapshots

Mahadi, Maaz; Ballal, Tarig; Moinuddin, Muhammad; Al-Naffouri, Tareq Y.; Al‐Saggaf, Ubaid M.

doi:10.23919/eusipco47968.2020.9287811

2020 28th European Signal Processing Conference (EUSIPCO) 2021

DOI: 10.23919/eusipco47968.2020.9287811

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A Robust LCMP Beamformer with Limited Snapshots

Maaz Mahadi

Tarig Ballal

Muhammad Moinuddin

et al.

Abstract: This paper deals with the problem of automatic diagonal loading for the linear constrained minimum power (LCMP) beamformer. To find the beamformer's weights, the LCMP problem is reformulated into its generalized sidelobe canceller implementation, which is an unconstrained least squares (LS) problem. To solve this problem, we utilize a bounded perturbation regularization (BPR) approach where a perturbation matrix with a bounded norm is added to the linear transformation matrix of the LS problem in order to enha… Show more

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2022

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Cited by 2 publications

References 24 publications

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Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

et al. 2022

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This paper is concerned with optimizing the weights of the global minimum-variance portfolio (GMVP) in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing the GMVP weights is highly influenced by the data covariance matrix estimation. In a high-dimensional setting, it is well known that the sample covariance matrix is not a proper estimator of the true covariance matrix since it is not invertible when we have fewer observations than the data dimension. Even with more observations, the sample covariance matrix may not be well-conditioned. This paper determines the GMVP weights based on a regularized covariance matrix estimator to overcome the abovementioned difficulties. Unlike other methods, the proper selection of the regularization parameter is achieved by minimizing the mean squared error of an estimate of the noise vector that accounts for the uncertainty in the data mean estimation. Using random-matrix-theory tools, we derive a consistent estimator of the achievable mean squared error that allows us to find the optimal regularization parameter using a simple line search. Simulation results demonstrate the effectiveness of the proposed method when the data dimension is larger than, or of the same order of, the number of data samples.INDEX TERMS Portfolio optimization, global minimum-variance portfolio, GMVP, random matrix theory, RMT, consistent estimator.

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Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

et al. 2022

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A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix

Zhou

Bao

2022

Applied Sciences

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In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.

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A Robust LCMP Beamformer with Limited Snapshots

Cited by 2 publications

References 24 publications

Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix

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