Results 51 to 60 of about 547,601 (228)
Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection
Sensors, 2019 This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance
Naixin Kang, Zheran Shang, Qinglei Du
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Sensors, 2022 Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by ...
Fuhai Wan, Jingwei Xu, Zhenrong Zhang
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Large Covariance Estimation by Thresholding Principal Orthogonal Complements [PDF]
, 2013 This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-
Fan, Jianqing +2 more
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Knowledge-aided STAP in heterogeneous clutter using a hierarchical bayesian algorithm [PDF]
, 2011 This paper addresses the problem of estimating the covariance matrix of a primary vector from heterogeneous samples and some prior knowledge, under the framework of knowledge-aided space-time adaptive processing (KA-STAP).
Besson, Olivier +2 more
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The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation
IEEE AccessVarious data analysis techniques and procedures (correlation heatmap, linear discriminant analysis, quadratic discriminant analysis) rely on the estimation of the covariance matrix or its inverse (the precision matrix).
Tuan L. Vo +5 more
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Tuning the Parameters for Precision Matrix Estimation Using Regression Analysis
IEEE Access, 2019 Precision matrix, i.e., inverse covariance matrix, is widely used in signal processing, and often estimated from training samples. Regularization techniques, such as banding and rank reduction, can be applied to the covariance matrix or precision matrix ...
Jun Tong +4 more
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Robustness Analysis Of Covariances Matrix Estimates
, 2010 Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark ...
Mahot, Mélanie +3 more
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Estimation of the Number of Spiked Eigenvalues in a Covariance Matrix by Bulk Eigenvalue Matching Analysis [PDF]
Journal of the American Statistical Association, 2020 The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, K.
Z. Ke, Yucong Ma, Xihong Lin
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Applied Sciences, 2021 For adaptive ultrasound imaging, a reliable estimation of the covariance matrix has a decisive influence on the performance of beamformers. In this paper, we propose a new cross subaperture averaging generalized sidelobe canceler approach (GSC-CROSS) for
Jin Yang +5 more
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When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators [PDF]
, 2010 The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization.
Lillo, Fabrizio +3 more
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