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Rank covariance matrix estimation of a partially known covariance matrix
Journal of Statistical Planning and Inference, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kuljus, Kristi, von Rosen, Dietrich
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DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
IEEE Signal Processing Letters, 2021In this paper, we discuss a new approach to direction of arrival estimation for systems with subarray sampling. We propose to estimate the covariance matrix of the full array from the sample covariance matrices of the subarrays using a neural network ...
Andreas Barthelme, W. Utschick
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Augmented Covariance Matrix Reconstruction for DOA Estimation Using Difference Coarray
IEEE Transactions on Signal Processing, 2021As is well known, nonuniform linear arrays have significant advantages in array aperture and degrees of freedom over uniform linear arrays. Using their difference coarrays, subspace-based approaches can be utilized to perform underdetermined and high ...
Zhi Zheng +3 more
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Kronecker Structured Covariance Matrix Estimation
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007The estimation of signal covariance matrices is a crucial part of many signal processing algorithms. In some applications, the structure of the problem suggests that the underlying, true, covariance matrix is the Kronecker product of two matrices. Examples of such problems are channel modelling for MIMO communications and signal modelling of EEG data ...
Karl Werner +2 more
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Covariance Matrix Estimation in Complex Surveys
The Egyptian Statistical Journal, 1989Summary: An estimator of the asymptotic covariance matrix of the 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.
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Joint DOA and Clutter Covariance Matrix Estimation in Compressive Sensing MIMO Radar
IEEE Transactions on Aerospace and Electronic Systems, 2019We apply the technique of compressive sensing (CS) to multiple-input multiple-output (MIMO) radars to estimate the direction of arrival (DOA) of potential targets embedded in cluttered environments using far fewer samples than the Nyquist rate ...
S. Salari +4 more
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Low-Rank Structured Covariance Matrix Estimation
IEEE Signal Processing Letters, 2019The covariance matrix estimation problem is posed in both the Bayesian and frequentist settings as the solution of a maximum a posteriori (MAP) or maximum likelihood (ML) optimization, respectively, when the true covariance consists of a known (or ...
Azer P. Shikhaliev +2 more
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Estimation of the Covariance Matrix
2020This chapter addresses decision-theoretic estimation of an error covariance matrix in a multivariate linear model relative to a Stein-type entropy loss. With a unified treatment for high and low dimensions, some important improving methods of the best scale and the best triangular invariant estimators are discussed by using the residual sum of squares ...
Hisayuki Tsukuma, Tatsuya Kubokawa
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An EL Approach for Similarity Parameter Selection in KA Covariance Matrix Estimation
IEEE Signal Processing Letters, 2019This letter deals with similarity parameter selection for knowledge-aided covariance matrix estimation in adaptive radar signal processing. Starting from the observation that the maximum likelihood estimate of the interference covariance matrix under a ...
Jianbo Li +3 more
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A Robust Heteroskedasticity Consistent Covariance Matrix Estimator
Statistics, 1997To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification ...
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