Results 21 to 30 of about 402,138 (284)
Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs
IET Signal Processing, 2022 This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs.
Xinghua Liu +5 more
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Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood [PDF]
, 2012 We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis (PCA) does not efficiently estimate the factor ...
Bai, Jushan, Liao, Yuan
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Covariance Estimation in High Dimensions via Kronecker Product Expansions [PDF]
, 2013 This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d.
Alfred O. Hero Iii +2 more
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Transposable regularized covariance models with an application to missing data imputation [PDF]
, 2010 Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features.
Allen, Genevera I., Tibshirani, Robert
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A subspace method for array covariance matrix estimation [PDF]
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016 This paper introduces a subspace method for the estimation of an array covariance matrix. It is shown that when the received signals are uncorrelated, the true array covariance matrices lie in a specific subspace whose dimension is typically much smaller than the dimension of the full space.
Rahmani, Mostafa, Atia, George K.
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Cholesky-based model averaging for covariance matrix estimation
Statistical Theory and Related Fields, 2017 Estimation of large covariance matrices is of great importance in multivariate analysis. The modified Cholesky decomposition is a commonly used technique in covariance matrix estimation given a specific order of variables.
Hao Zheng +3 more
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Sensors, 2020 Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space ...
Chenghao Shan +3 more
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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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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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Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors [PDF]
مدیریت مهندسی و رایانش نرم, 2023 Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference.
Saman Rezaeizadeh, Mehdi Bekrani
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