Results 1 to 10 of about 623,991 (302)
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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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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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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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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Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations
IEEE Access, 2020 Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise ...
Yanmin Zhu +3 more
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A finite-difference method for linearization in nonlinear estimation algorithms [PDF]
Modeling, Identification and Control, 1998 Linearizations of nonlinear functions that are based on Jacobian matrices often cannot be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper.
Tor S. Schei
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On the Properties of Estimates of Monotonic Mean Vectors for Multivariate Normal Distributions [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 .Problems concerning estimation of parameters and determination the statistic, when it is known a priori that some of these parameters are subject to certain order restrictions, are of considerable interest.
Abouzar Bazyari
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Remote Sensing, 2022 Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds.
Ronghua Yang +3 more
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IEEE Access, 2022 Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks.
Imtiaz Nasim, Ahmed S. Ibrahim
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Local Laws for Sparse Sample Covariance Matrices
Mathematics, 2022 We proved the local Marchenko–Pastur law for sparse sample covariance matrices that corresponded to rectangular observation matrices of order n×m with n/m→y (where y>0) and sparse probability npn>logβn (where β>0).
Alexander N. Tikhomirov +1 more
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