Estimating the covariance of random matrices
Electronic Journal of Probability, 2013 We extend to the matrix setting a recent result of Srivastava-Vershynin about estimating the covariance matrix of a random vector. The result can be in- terpreted as a quantified version of the law of large numbers for positive semi-definite matrices which verify some regularity assumption. Beside giving examples, we dis- cuss the notion of log-concave
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An interval Kalman filter enhanced by lowering the covariance matrix upper bound
International Journal of Applied Mathematics and Computer Science, 2021 This paper proposes a variance upper bound based interval Kalman filter that enhances the interval Kalman filter based on the same principle proposed by Tran et al. (2017) for uncertain discrete time linear models.
Tran Tuan Anh +3 more
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Covariance Estimation: The GLM and Regularization Perspectives [PDF]
, 2011 Finding an unconstrained and statistically interpretable reparameterization of a covariance matrix is still an open problem in statistics. Its solution is of central importance in covariance estimation, particularly in the recent high-dimensional data ...
Pourahmadi, Mohsen
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The Research on Distributed Fusion Estimation Based on Machine Learning
IEEE Access, 2020 Multi-sensor distributed fusion estimation algorithms based on machine learning are proposed in this paper. Firstly, using local estimations as inputs and estimations of three classic distributed fusion (weighted by matrices, by diagonal matrices and by ...
Zhengxiao Peng, Yun Li, Gang Hao
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On the Significance of Covariance for Constraining Theoretical Models from Galaxy Observables
The Astrophysical JournalIn this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite.
Yongseok Jo +3 more
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Positive semidefiniteness of estimated covariance matrices in linear models for sample survey data
Special Matrices, 2016 Descriptive analysis of sample survey data estimates means, totals and their variances in a design framework. When analysis is extended to linear models, the standard design-based method for regression parameters includes inverse selection probabilities ...
Haslett Stephen
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Radar Target Detection Method of Aircraft Wake Vortices Based on Matrix Information Geometry
Leida xuebao, 2017 The application of matrix information geometry to radar signal processing and target detection is a new and interesting subject. Wake vortices are Doppler-spread after Fourier transform.
Liu Junkai +4 more
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Covariance Matrix Reconstruction to Improve DoA Estimation Using Subspace Method in Low SNR Regime
IEEE AccessTraditional Direction of Arrival (DoA) estimation methods, such as Multiple Signal Classification Algorithm (MUSIC), Root MUSIC (R-MUSIC), and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), often suffer significant ...
Sunita Khichar +2 more
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Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures
Applied Computational Intelligence and Soft Computing, 2012 This paper presents a novel computational approach for estimating fuzzy measures directly from Gaussian mixtures model (GMM). The mixture components of GMM provide the membership functions for the input-output fuzzy sets. By treating consequent part as a
Nishchal K. Verma
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Initial covariance estimation and analysis for EKF localization using route-based experimental data
Franklin OpenReliable initialization of covariance matrices is crucial for accurate state estimation in extended Kalman filter (EKF) applications. However, many previous works did not technically formulate but rather relied on assumed or arbitrary covariance values ...
Muhammad Haniff Gusrial +4 more
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