Results 31 to 40 of about 402,138 (284)
Analysis of Semi-Blind Channel Estimation in Multiuser Massive MIMO Systems With Perturbations
IEEE Access, 2019 In the massive multiple-input multiple-output (MIMO) systems, pilot contamination and signal perturbation are two important issues in the semi-blind channel estimation methods.
Cheng Hu, Hong Wang, Rongfang Song
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IET Radar, Sonar & Navigation, 2021 To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN)
Hao Xiao, Tong Wang, Cai Wen, Bing Ren
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High-Dimensional Covariance Estimation via Constrained Lq-Type Regularization
Mathematics, 2023 High-dimensional covariance matrix estimation is one of the fundamental and important problems in multivariate analysis and has a wide range of applications in many fields.
Xin Wang +3 more
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Remote Sensing, 2020 Synthetic aperture radar (SAR) tomography has shown great potential in multi-dimensional monitoring of urban infrastructures and detection of their possible slow deformations. Along this line, undeniable improvements in SAR tomography (TomoSAR) detection
Hossein Aghababaei
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Blind Estimation of Spreading Code Sequence of QPSK-DSSS Signal Based on Fast-ICA
Information, 2023 Most of the existing estimation methods of spreading code sequence are not suitable for the QPSK-DSSS. We propose a spreading code sequence estimation method based on fast independent component analysis (Fast-ICA). It mainly includes signal preprocessing,
Lu Xu, Xiaxia Liu, Yijia Zhang
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Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection
Sensors, 2019 This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance
Naixin Kang, Zheran Shang, Qinglei Du
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Estimating model error covariance matrix parameters in extended Kalman filtering [PDF]
Nonlinear Processes in Geophysics, 2014 The extended Kalman filter (EKF) is a popular state estimation method for nonlinear dynamical models. The model error covariance matrix is often seen as a tuning parameter in EKF, which is often simply postulated by the user.
A. Solonen +4 more
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Sensors, 2022 Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by ...
Fuhai Wan, Jingwei Xu, Zhenrong Zhang
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Shrinkage Estimation of the Power Spectrum Covariance Matrix [PDF]
, 2008 We seek to improve estimates of the power spectrum covariance matrix from a limited number of simulations by employing a novel statistical technique known as shrinkage estimation.
Adrian C. Pope +14 more
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High‐dimensional covariance matrix estimation [PDF]
WIREs Computational Statistics, 2019 AbstractCovariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of
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