Results 31 to 40 of about 547,601 (228)
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood [PDF]
, 2012 We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis (PCA) does not efficiently estimate the factor ...
Bai, Jushan, Liao, Yuan
core +2 more sources
IET radar, sonar & navigation, 2020 Owing to covariance matrix estimation error, desired signal steering vector mismatch, and the existence of target signal in training samples, most of adaptive beamforming algorithms suffer from a great performance degradation.
Junsheng Huang, Hongtao Su, Yang Yang
semanticscholar +1 more source
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 +5 more
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
Sensors, 2020 Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space ...
Chenghao Shan +3 more
doaj +1 more source
Shrinkage Estimation of the Power Spectrum Covariance Matrix [PDF]
, 2008 We seek to improve estimates of the power spectrum covariance matrix from a limited number of simulations by employing a novel statistical technique known as shrinkage estimation.
Adrian C. Pope +14 more
core +1 more source
New properties for Tyler's covariance matrix estimator [PDF]
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016 In this paper, we deal with covariance matrix estimation in complex elliptically symmetric (CES) distributions. We focus on Tyler's estimator (TyE) and the well-known sample covariance matrix (SCM). TyE is widely used in practice, but its statistical behavior is still poorly understood.
Draskovic, Gordana, Pascal, Frédéric
openaire +2 more sources
Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Journal of Electrical and Computer Engineering, 2012 Accurate covariance matrix estimation for high-dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image ...
Nir Gorelik +3 more
doaj +1 more source
Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors [PDF]
مدیریت مهندسی و رایانش نرم, 2023 Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference.
Saman Rezaeizadeh, Mehdi Bekrani
doaj
A Robust Statistics Approach to Minimum Variance Portfolio Optimization [PDF]
, 2015 We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns.
Couillet, Romain +2 more
core +4 more sources