Results 71 to 80 of about 547,601 (228)
EURASIP Journal on Advances in Signal Processing, 2020 Due to the rapid development and wide application of compressed sensing and sparse reconstruction theory, there exists a series of sparsity-based methods for the antenna sensor array direction of arrival (DOA) estimation with excellent performance ...
Tao Chen, Lin Shi, Yongzhi Yu
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Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems
Mathematics, 2023 This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance.
Shoudong Wang, Binqiang Xue
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Random matrix-improved estimation of covariance matrix distances [PDF]
Journal of Multivariate Analysis, 2019 Given two sets $x_1^{(1)},\ldots,x_{n_1}^{(1)}$ and $x_1^{(2)},\ldots,x_{n_2}^{(2)}\in\mathbb{R}^p$ (or $\mathbb{C}^p$) of random vectors with zero mean and positive definite covariance matrices $C_1$ and $C_2\in\mathbb{R}^{p\times p}$ (or $\mathbb{C}^{p\times p}$), respectively, this article provides novel estimators for a wide range of distances ...
Couillet, Romain +3 more
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Nonlinear shrinkage estimation of large-dimensional covariance matrices [PDF]
, 2011 Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer ...
Ledoit, Olivier, Wolf, Michael
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IEEE Access, 2019 The covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar.
Fangqing Wen, Zijing Zhang, Gong Zhang
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Remote Sensing, 2023 In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models ...
Naixin Kang +3 more
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Group Lasso estimation of high-dimensional covariance matrices [PDF]
, 2010 In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a ...
Alvarez, Lilian Muniz +3 more
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IET Radar, Sonar & NavigationTo address the array aperture loss caused by mainstream direction of arrival (DOA) estimation algorithms for coherent signals using matrix interpolation techniques, a non‐circular (NC) coherent signal direction‐finding method that fully utilises ...
Zihan Shen +3 more
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Estimating Mean and Covariance Structure with Reweighted Least Squares [PDF]
, 2020 Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual ...
Zheng, Bang Quan
core
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe polarimetric synthetic aperture radar tomography (TomoSAR) technique has proven to be a highly promising cutting-edge microwave remote sensing technique for obtaining forest vertical structure parameters because of its ability in three-dimensional ...
Youjun Wang +7 more
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