Results 41 to 50 of about 547,601 (228)

Optimal Shrinkage Covariance Matrix Estimation Under Random Sampling From Elliptical Distributions [PDF]

IEEE Transactions on Signal Processing, 2018
This paper considers the problem of estimating a high-dimensional covariance matrix in a low sample support situation where the sample size is smaller, or not much larger, than the dimensionality of the data, which could potentially be very large.
E. Ollila, Elias Raninen
semanticscholar   +1 more source

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
doaj   +1 more source

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

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
doaj   +1 more source

Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings [PDF]

, 2014
Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a high-dimensional covariance matrix are often employed to circumvent the ...
Touloumis, Anestis
core   +2 more sources

High-Dimensional Covariance Estimation via Constrained L_q-Type Regularization

Mathematics, 2023
High-dimensional covariance matrix estimation is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields.
Xin Wang, Lingchen Kong, Liqun Wang, Zhaoqilin Yang   +3 more
doaj   +1 more source

Adaptive covariance matrix estimation through block thresholding [PDF]

, 2012
Estimation of large covariance matrices has drawn considerable recent attention, and the theoretical focus so far has mainly been on developing a minimax theory over a fixed parameter space.
Cai, T. Tony, Yuan, Ming
core   +3 more sources

On the Assessment of Non-Local Multi-Looking in Detection of Persistent Scatterers Using SAR Tomography

Remote Sensing, 2020
Synthetic aperture radar (SAR) tomography has shown great potential in multi-dimensional monitoring of urban infrastructures and detection of their possible slow deformations. Along this line, undeniable improvements in SAR tomography (TomoSAR) detection
Hossein Aghababaei
doaj   +1 more source

Estimating model error covariance matrix parameters in extended Kalman filtering [PDF]

Nonlinear Processes in Geophysics, 2014
The extended Kalman filter (EKF) is a popular state estimation method for nonlinear dynamical models. The model error covariance matrix is often seen as a tuning parameter in EKF, which is often simply postulated by the user.
A. Solonen, J. Hakkarainen, A. Ilin, M. Abbas, A. Bibov   +4 more
doaj   +1 more source

Blind Estimation of Spreading Code Sequence of QPSK-DSSS Signal Based on Fast-ICA

Information, 2023
Most of the existing estimation methods of spreading code sequence are not suitable for the QPSK-DSSS. We propose a spreading code sequence estimation method based on fast independent component analysis (Fast-ICA). It mainly includes signal preprocessing,
Lu Xu, Xiaxia Liu, Yijia Zhang
doaj   +1 more source

Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices [PDF]

, 2015
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai, Tony, Ma, Zongming, Wu, Yihong
core   +3 more sources