Results 51 to 60 of about 3,211,049 (333)
International Journal of Adaptive Control and Signal Processing, 2019 This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems.
Xiao Zhang, F. Ding, Erfu Yang
semanticscholar +1 more source
Wiener Filter Approximations Without Covariance Matrix Inversion
IEEE Open Journal of Signal Processing, 2023 In this article, we address the problem of ill-conditioning of the Wiener filter, the optimal linear minimum mean square error estimator. Computing the Wiener filter involves the inverse of the observation covariance matrix.
Pranav U. Damale +2 more
doaj +1 more source
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices [PDF]
, 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, Tony, Ma, Zongming, Wu, Yihong
core +3 more sources
Covariance Matrix Estimation in Massive MIMO [PDF]
IEEE Signal Processing Letters, 2017 Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum
David Neumann, M. Joham, W. Utschick
semanticscholar +1 more source
A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems [PDF]
IEEE Transactions on Signal Processing, 2017 A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix ...
A. Aubry, A. De Maio, L. Pallotta
semanticscholar +1 more source
Whitening Degree Evaluation Method to Test Estimate Accuracy of Speckle Covariance Matrix
Leida xuebao, 2017 In the background of sea clutter, the accuracy of adaptive target detection is heavily influenced by the estimated performance of speckle covariance matrix.
Yu Han +3 more
doaj +1 more source
Purity and Covariance Matrix [PDF]
Journal of Russian Laser Research, 2014 Basing on the simplest single-mode field source, we investigate the role of the various covariance matrices for reconstructing the field state and describing its quantum statistical properties. In spite of the fact that the intracavity field is a single-mode field, we take into account the natural multimode structure arising in the field, when it ...
Golubeva, T., Golubev, Yu.
openaire +2 more sources
Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method
Leida xuebao, 2023 The realization of anti-jamming technologies via beamforming for applications in Frequency-Diverse Arrays and Multiple-Input and Multiple-Output (FDA-MIMO) radar is a field that is undergoing intensive research.
Zihang DING, Junwei XIE, Bo WANG
doaj +1 more source
Physical properties of the Schur complement of local covariance matrices [PDF]
, 2007 General properties of global covariance matrices representing bipartite Gaussian states can be decomposed into properties of local covariance matrices and their Schur complements. We demonstrate that given a bipartite Gaussian state $\rho_{12}$ described
Eisert J Wolf M M +7 more
core +2 more sources
Matrix Completion With Covariate Information
Journal of the American Statistical Association, 2018 This paper investigates the problem of matrix completion from corrupted data, when additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with
Xiaojun Mao +2 more
openaire +1 more source