Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter [PDF]
Sensors, 2018 Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by
Alexis Nez +4 more
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Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion [PDF]
Sensors, 2021 Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted ...
Christopher Funk +2 more
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Improving the condition number of estimated covariance matrices [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2020 High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data assimilation procedures.
Jemima M. Tabeart +4 more
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HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification [PDF]
MethodsX, 2020 We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement. • Compute matrix-
Alexander Litvinenko +4 more
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Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices. [PDF]
Probab Theory Relat Fields, 2015 This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai T, Ma Z, Wu Y.
europepmc +4 more sources
A simple procedure for the comparison of covariance matrices [PDF]
BMC Evolutionary Biology, 2012 Background Comparing the covariation patterns of populations or species is a basic step in the evolutionary analysis of quantitative traits. Here I propose a new, simple method to make this comparison in two population samples that is based on comparing ...
Garcia Carlos
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2D-FFTLog: efficient computation of real-space covariance matrices for galaxy clustering and weak lensing [PDF]
Monthly notices of the Royal Astronomical Society, 2020 Accurate covariance matrices for two-point functions are critical for inferring cosmological parameters in likelihood analyses of large-scale structure surveys.
Xiao Fang (方啸), T. Eifler, E. Krause
semanticscholar +1 more source
Statistical inference for principal components of spiked covariance matrices [PDF]
Annals of Statistics, 2020 In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the high dimensional spiked sample covariance matrices, in the supercritical case when a reliable detection of spikes is possible.
Z. Bao +3 more
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Covariance Shaping Over Riemannian Manifolds for Massive MIMO Communication
IEEE Access, 2023 Acquiring accurate instantaneous channel state information (CSI) is a challenging aspect of massive multi-input multi-output (MIMO) communication. Utilizing statistical information, such as channel covariance matrix, to design statistical beamforming ...
Joarder Jafor Sadique +2 more
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Cosmological constraints from BOSS with analytic covariance matrices [PDF]
, 2020 We use analytic covariance matrices to carry out a full-shape analysis of the galaxy power spectrum multipoles from the Baryon Oscillation Spectroscopic Survey (BOSS).
D. Wadekar, M. Ivanov, R. Scoccimarro
semanticscholar +1 more source