covariance matrix estimation method
A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar
... iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is ...this method, the clutter covariance matrix is estimated by the original ...
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Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
... a covariance matrix efficiently and discovering its structure are im- portant statistical problems with applications in many ...the covariance matrix of Gaussian ...the covariance ...
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Off -Grid Direction-of-Arrival Estimation Using a Sparse Array Covariance Matrix
... (DOA) estimation is of great importance in applications of radar, sonar, and communication ...MUSIC method is proven to be equivalent to the maximum likelihood (ML) method ...
6
A novel Architecture for DOA estimation of signals to track target source positions
... Abstract— MUSIC is traditionally considered better than other algorithms for DOAs. With the increasing demand of near perfect target localisation, and less interference at higher noise regions, coupled with ability to ...
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Group Lasso Estimation of High-dimensional Covariance Matrices
... a matrix regression model as in Bigot et ...high-dimensional covariance matrix estimation based on empirical contrast regularization by a group Lasso ...the method selects a sparse set ...
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High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
... proposed method and compare it with other popular alternatives, we now conduct a set of numerical ...variance covariance matrix given by Σ 0 i j = ρ |i− j| for some ρ 6= ...Such covariance ...
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Recursive Adaptive Algorithms for Fast and Rapidly Time-Varying Systems
... One method is to adaptively adjust the forgetting factor at the rapidly changing points while keeping its nominal values at other locations ...the estimation co- variance matrix or at the locations ...
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Band Width Selection for High Dimensional Covariance Matrix Estimation
... comparison was made under the Gaussian innovation (∆ = 0), and the standardized Gamma innovations with ∆ = 6, 12, 20 and 60. We recall that ∆ measures the excessive kurtosis over that of the Gaussian. We observed that ...
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Automatic positive semidefinate HAC covariance matrix and GMM estimation
... ! covariance matrix estimators + The standard HAC estimation method reweights estimators of the autocovariances + Here we initially smooth the data observations themselves using kernel ...
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Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation
... factor matrix via the kernel smooth- ing estimation, which was further developed by Rothman, Levina and Zhu ...sample covariance matrix directly for estimating Σ and banding the Cholesky ...
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On errors in variables estimation with unknown noise variance ratio
... proposed estimation method for static EIV models and states conditions for con- ...the method to dynamic models and Section 5 shows simulation examples for EIV system ...proposed method is ...
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Comparison of methods for estimation of genetic covariance matrix from SNP or pedigree data utilised to predict breeding value
... Genetic covariance matrices between all animals present in the dataset were estimated with three ...based method - PB) was based on the additive relationship matrix calculated from pedi- ...Second ...
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Eigenvalue regularized covariance matrix estimators for high dimensional data
... regularized estimation of covariance or precision matrices with special structures, such as banded (Bickel and Levina, 2008b) or sparse covariance matrix (Bickel and Levina, 2008a; Cai and ...
175
A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
... of covariance and inverse covariance matrix estimation, where often the interest is to estimate a sparse matrix, the proposed method is flexible in estimat- ing both a sparse and ...
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An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application
... The estimation errors are gauged in the following norms: k Σ b > u − Σ u k, k( Σ b > ) −1 − Σ −1 k and k Σ b > − Σk Σ as shown in Theorem ...The estimation errors of applying sample ...
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Contributions to high dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand
... group covariance components. The problem of estimating the large covariance matrices has long been known in the literature of multivariate ...of covariance matrices become ill-conditioned (when n is ...
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The Direction-of-Arrival and Polarization Estimation Using Coprime Array: a Reconstructed Covariance Matrix Approach
... proposed method versus the SNR is ...proposed method yields more accurate results than those of the conventional method because there are no irregular spurious peaks around the signal response peaks ...
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Autocorrelation based factor analysis and nonlinear shrinkage estimation of large integrated covariance matrix
... integrated covariance matrix asymptotically almost surely with shrinkage of eigenvalues achieved nonlinearly, while local integrated covolatilities are adapted and estimated ...Our method is fast ...
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CS-SFD Algorithm for GNSS Anti-Jamming Receivers
... space-time covariance matrix is perfectly ...the covariance matrix in the case of fewer snapshots, which may lead to remarkable performance ...proposed method utilizes less sampled data ...
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Low-Complexity Direction-of-Arrival Estimation Based on Wideband Co-Prime Arrays
... (DOA) estimation methods for wideband co- prime arrays is ...narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and ...
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