Results 91 to 100 of about 547,601 (228)

Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming

IEEE Access, 2019
Recently, a number of robust adaptive beamforming (RAB) methods based on Capon power spectrum estimator integrated over a specific region for covariance matrix reconstruction have been proposed.
Xingyu Zhu, Zhongfu Ye, Xu Xu, Rui Zheng
doaj   +1 more source

Subspace Leakage Analysis and Improved DOA Estimation with Small Sample Size

, 2015
Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix.
Shaghaghi, Mahdi, Vorobyov, Sergiy A.
core   +1 more source

Stochastic Block Covariance Matrix Estimation

Motivated by a neuroscience application we study the problem of statistical estimation of a high-dimensional covariance matrix with a block structure. The block model embeds a structural assumption: the population of items (neurons) can be divided into latent sub-populations with shared associative covariation within blocks and shared associative or ...
Chen, Yunran, Tokdar, Surya T, Groh, Jennifer M   +2 more
openaire   +2 more sources

Optimal algorithm for distributed scatterer InSAR phase estimation based on cross-correlation complex coherence matrix

International Journal of Applied Earth Observations and Geoinformation
Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods
Dingyi Zhou, Zhifang Zhao
doaj   +1 more source

Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided Space-Time Adaptive Processing

IEEE Access, 2018
In space–time adaptive processing (STAP) technique, the estimation of the interference-plus-noise covariance matrix is one of the critical points.
Yu Zhao, Shaohua Wan, Songtao Lu, Jinping Sun, Peng Lei   +4 more
doaj   +1 more source

Covariance estimation for multivariate conditionally Gaussian dynamic linear models

, 2007
In multivariate time series, the estimation of the covariance matrix of the observation innovations plays an important role in forecasting as it enables the computation of the standardized forecast error vectors as well as it enables the computation of ...
Anderson, Barassi, Barbosa, Carter, Dahlhaus, Dickey, Doucet, Durbin, Fernández, Francq, Frühwirth-Schnatter, Gamerman, Godolphin, Gravelle, Gupta, Harvey, Harvey, Harvey, Harvey, Huerta, Johansen, Kadiyala, Kadiyala, Kitagawa, Litterman, Lütkepohl, Moauro, Ni, Ooms, Pollock, Queen, Quintana, Quintana, Salvador, Salvador, Salvador, Shumway, Sun, Triantafyllopoulos, Triantafyllopoulos, Uhlig, Watkins, West, West, Whittle   +44 more
core   +1 more source

Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence

Entropy, 2018
This paper presents a covariance matrix estimation method based on information geometry in a heterogeneous clutter. In particular, the problem of covariance estimation is reformulated as the computation of geometric median for covariance matrices ...
Xiaoqiang Hua, Yongqiang Cheng, Hongqiang Wang, Yuliang Qin   +3 more
doaj   +1 more source

The Masked Sample Covariance Estimator: An Analysis via Matrix Concentration Inequalities [PDF]

, 2011
Covariance estimation becomes challenging in the regime where the number p of variables outstrips the number n of samples available to construct the estimate. One way to circumvent this problem is to assume that the covariance matrix is nearly sparse and
Chen, Richard Y., Gittens, Alex, Tropp, Joel A.   +2 more
core   +1 more source

Local quadratic spectral and covariance matrix estimation

Journal of Time Series Analysis
The problem of estimating the spectral density matrix of a multi‐variate time series is revisited with special focus on the frequencies and . Recognizing that the entries of the spectral density matrix at these two boundary points are real‐valued, we propose a new estimator constructed from a local polynomial regression of the real portion of the ...
Tucker McElroy, Dimitris N. Politis
openaire   +2 more sources

Direction of Arrival Estimation in Low-Grazing Angle: A Partial Spatial-Differencing Approach

IEEE Access, 2017
This paper addresses a partial spatial-differencing (PSD) approach for the direction of arrival estimation in a low-grazing angle (LGA) condition. By dividing the sample covariance matrix into several column subvectors, we first form the corresponding ...
Junpeng Shi, Guoping Hu, Xiaofei Zhang
doaj   +1 more source