Results 1 to 10 of about 3,390,517 (303)

Covariance Matrix Estimation in Complex Surveys [PDF]

The Egyptian Statistical Journal, 1989
An estimator of asymptotic covariance matrix of vector of second-order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
Muhammad Pervaiz
doaj   +3 more sources

A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm.
Tianfu Zhang, Zhihao Wang, Ning Qiao, Shuangxi Zhang, Mengdao Xing, Yongliang Wang   +5 more
doaj   +3 more sources

Gridless DOA Estimator for 1.5-Bit Sparse Massive MIMO Systems Based on Covariance Matrix Estimation [PDF]

Entropy
To reduce the hardware cost of massive multiple-input multiple-output (MIMO) systems, low-bit analog-to-digital converters (ADCs) and sparse arrays are widely used.
Yuan Peng, Xiongbo Zheng, Zhiyong Cheng
doaj   +2 more sources

k-Covariance: An Approach of Ensemble Covariance Estimation and Undersampling to Stabilize the Covariance Matrix in the Global Minimum Variance Portfolio

Applied Sciences, 2022
A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this ...
Tuan Tran, Nhat Nguyen, Trung Nguyen
doaj   +1 more source

2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion

Sensors, 2022
In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains.
Ruru Mei, Ye Tian, Yonghui Huang, Zhugang Wang   +3 more
doaj   +1 more source

DOA-Estimation Method Based on Improved Spatial-Smoothing Technique

Mathematics, 2023
To improve the data utilization of the sensor array and direction-of-arrival-(DOA)-estimation performance for coherent signals, a DOA-estimation method with a modified spatial-smoothing technique is proposed. The covariance matrix of the received data of
Yujun Hou, Xuhu Wang, Lei Ding, Xu Jin, Qunfei Zhang   +4 more
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The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise

Sensors, 2022
Due to the discontinuity of ocean waves and mountains, there are often multipath propagation effects and obvious pulse characteristics in low-altitude detection.
Bin Lin, Guoping Hu, Hao Zhou, Guimei Zheng, Yuwei Song   +4 more
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Covariance estimation via fiducial inference

Statistical Theory and Related Fields, 2021
As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi, Jan Hannig, Randy C. S. Lai, Thomas C. M. Lee   +3 more
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Improved Large Dynamic Covariance Matrix Estimation With Graphical Lasso and Its Application in Portfolio Selection

IEEE Access, 2020
The estimation of the large and high-dimensional covariance matrix and precision matrix is a fundamental problem in modern multivariate analysis. It has been widely applied in economics, finance, biology, social networks and health sciences. However, the
Xin Yuan, Weiqin Yu, Zhixiang Yin, Guoqiang Wang   +3 more
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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, Jianwei Guan, Rui Jiang, Xiang Gao, Badong Chen, Shuzhi Sam Ge   +5 more
doaj   +1 more source