Results 21 to 30 of about 140,209 (263)
Analysis of Semi-Blind Channel Estimation in Multiuser Massive MIMO Systems With Perturbations
IEEE Access, 2019 In the massive multiple-input multiple-output (MIMO) systems, pilot contamination and signal perturbation are two important issues in the semi-blind channel estimation methods.
Cheng Hu, Hong Wang, Rongfang Song
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Discriminant methods for high dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is singular.
Poompong Kaewumpai, Samruam Chongcharoen
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Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Journal of Electrical and Computer Engineering, 2012 Accurate covariance matrix estimation for high-dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image ...
Nir Gorelik +3 more
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Covariance estimation via fiducial inference
Statistical Theory and Related Fields, 2021 As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi +3 more
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IET Radar, Sonar & Navigation, 2021 To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN)
Hao Xiao, Tong Wang, Cai Wen, Bing Ren
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IEEE Access, 2018 The performance of the conventional beamformers degrades in the presence of desired signal in the data samples and array steering vector (ASV) mismatch. Many beamformers have been proposed to improve the performance of standard Capon beamformer. However,
Denis Igambi, Xiaopeng Yang, Babur Jalal
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BAYESIAN INFERENCE FOR A COVARIANCE MATRIX
Conference on Applied Statistics in Agriculture, 2014 Final version, already published in proceedings, Proceedings of 26th Annual Conference on Applied Statistics in Agriculture.
Alvarez, Ignacio +2 more
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Regularization for high-dimensional covariance matrix
Special Matrices, 2016 In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n.
Cui Xiangzhao +5 more
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Wiener Filter Approximations Without Covariance Matrix Inversion
IEEE Open Journal of Signal Processing, 2023 In this article, we address the problem of ill-conditioning of the Wiener filter, the optimal linear minimum mean square error estimator. Computing the Wiener filter involves the inverse of the observation covariance matrix.
Pranav U. Damale +2 more
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Regularized Tapered Sample Covariance Matrix
IEEE Transactions on Signal Processing, 2022 Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer).
Breloy, Arnaud, Ollila, Esa
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