Results 41 to 50 of about 2,877,467 (378)
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
core +3 more sources
International Journal of Adaptive Control and Signal Processing, 2019 This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems.
Xiao Zhang, F. Ding, Erfu Yang
semanticscholar +1 more source
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
doaj +1 more source
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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Covariance Matrix Estimation in Massive MIMO [PDF]
IEEE Signal Processing Letters, 2017 Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum
David Neumann, M. Joham, W. Utschick
semanticscholar +1 more source
Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix
Leida xuebao, 2017 In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix.
Yu Han +3 more
doaj +1 more source
An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter [PDF]
Nonlinear Processes in Geophysics, 2017 The ensemble Kalman filter (EnKF) is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts.
G. Wu, G. Wu, X. Zheng
doaj +1 more source
A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems [PDF]
IEEE Transactions on Signal Processing, 2017 A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix ...
A. Aubry, A. De Maio, L. Pallotta
semanticscholar +1 more source
Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method
Leida xuebao, 2023 The realization of anti-jamming technologies via beamforming for applications in Frequency-Diverse Arrays and Multiple-Input and Multiple-Output (FDA-MIMO) radar is a field that is undergoing intensive research.
Zihang DING, Junwei XIE, Bo WANG
doaj +1 more source
Purity and Covariance Matrix [PDF]
Journal of Russian Laser Research, 2014 Basing on the simplest single-mode field source, we investigate the role of the various covariance matrices for reconstructing the field state and describing its quantum statistical properties. In spite of the fact that the intracavity field is a single-mode field, we take into account the natural multimode structure arising in the field, when it ...
Golubeva, T., Golubev, Yu.
openaire +2 more sources