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Journal articles on the topic 'Compressive covariance estimation'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Azizyan, Martin, Akshay Krishnamurthy, and Aarti Singh. "Extreme Compressive Sampling for Covariance Estimation." IEEE Transactions on Information Theory 64, no. 12 (2018): 7613–35. http://dx.doi.org/10.1109/tit.2018.2871077.

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Alwan, Nuha A. S. "Compressive Covariance Sensing-Based Power Spectrum Estimation of Real-Valued Signals Subject to Sub-Nyquist Sampling." Modelling and Simulation in Engineering 2021 (April 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/5511486.

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In this work, an estimate of the power spectrum of a real-valued wide-sense stationary autoregressive signal is computed from sub-Nyquist or compressed measurements in additive white Gaussian noise. The problem is formulated using the concepts of compressive covariance sensing and Blackman-Tukey nonparametric spectrum estimation. Only the second-order statistics of the original signal, rather than the signal itself, need to be recovered from the compressed signal. This is achieved by solving the resulting overdetermined system of equations by application of least squares, thereby circumventing

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3

Pourkamali‐Anaraki, Farhad. "Estimation of the sample covariance matrix from compressive measurements." IET Signal Processing 10, no. 9 (2016): 1089–95. http://dx.doi.org/10.1049/iet-spr.2016.0169.

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4

Liu, Aihua, Qiang Yang, Xin Zhang, and Weibo Deng. "Direction-of-Arrival Estimation for Coprime Array Using Compressive Sensing Based Array Interpolation." International Journal of Antennas and Propagation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6425067.

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A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA) of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC) applied on the continuous lags in the VSNLA; the degrees of freedom (DoFs) for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs off

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Prasanna, Dheeraj, and Chandra R. Murthy. "mmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation." IEEE Transactions on Signal Processing 69 (2021): 2356–70. http://dx.doi.org/10.1109/tsp.2021.3070210.

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6

Li, Jian Feng, Xiao Fei Zhang, and Tong Hu. "Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots." Applied Mechanics and Materials 347-350 (August 2013): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1028.

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The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the propos

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7

Wang, Ruisong, Gongliang Liu, Wenjing Kang, Bo Li, Ruofei Ma, and Chunsheng Zhu. "Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks." Sensors 18, no. 8 (2018): 2568. http://dx.doi.org/10.3390/s18082568.

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Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this p

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Monsalve, Jonathan, Juan Ramirez, Inaki Esnaola, and Henry Arguello. "Covariance Estimation From Compressive Data Partitions Using a Projected Gradient-Based Algorithm." IEEE Transactions on Image Processing 31 (2022): 4817–27. http://dx.doi.org/10.1109/tip.2022.3187285.

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Salari, Soheil, Francois Chan, Yiu-Tong Chan, Il-Min Kim, and Roger Cormier. "Joint DOA and Clutter Covariance Matrix Estimation in Compressive Sensing MIMO Radar." IEEE Transactions on Aerospace and Electronic Systems 55, no. 1 (2019): 318–31. http://dx.doi.org/10.1109/taes.2018.2850459.

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Alwan, Nuha A. S., and Zahir M. Hussain. "Frequency Estimation from Compressed Measurements of a Sinusoid in Moving-Average Colored Noise." Electronics 10, no. 15 (2021): 1852. http://dx.doi.org/10.3390/electronics10151852.

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Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency esti

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11

Smriti, Sahu, and Rayavarapu Neela. "Compressive speech enhancement using semi-soft thresholding and improved threshold estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2788–800. https://doi.org/10.11591/ijece.v13i3.pp2788-2800.

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Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basisfunction based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silenceregion of the signa

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Paik, Ji Woong, Joon-Ho Lee, and Wooyoung Hong. "An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix." Sensors 21, no. 13 (2021): 4403. http://dx.doi.org/10.3390/s21134403.

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An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In t

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13

Zhang, Yahao, Yixin Yang, Long Yang, and Yong Wang. "Direction-of-arrival estimation for coherent signals through covariance-based grid free compressive sensing." JASA Express Letters 1, no. 9 (2021): 094801. http://dx.doi.org/10.1121/10.0006389.

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14

Park, Sungwoo, and Robert W. Heath. "Spatial Channel Covariance Estimation for the Hybrid MIMO Architecture: A Compressive Sensing-Based Approach." IEEE Transactions on Wireless Communications 17, no. 12 (2018): 8047–62. http://dx.doi.org/10.1109/twc.2018.2873592.

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15

Sahu, Smriti, and Neela Rayavarapu. "Compressive speech enhancement using semi-soft thresholding and improved threshold estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2788. http://dx.doi.org/10.11591/ijece.v13i3.pp2788-2800.

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<span lang="EN-US">Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-

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16

Paik, Ji Woong, Wooyoung Hong, Jae-Kyun Ahn, and Joon-Ho Lee. "Statistics on noise covariance matrix for covariance fitting-based compressive sensing direction-of-arrival estimation algorithm: For use with optimization via regularization." Journal of the Acoustical Society of America 143, no. 6 (2018): 3883–90. http://dx.doi.org/10.1121/1.5042354.

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17

Yao, Di, Xin Zhang, Bin Hu, Qiang Yang, and Xiaochuan Wu. "Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors." Sensors 20, no. 10 (2020): 2930. http://dx.doi.org/10.3390/s20102930.

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An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) matrix reconstruction method, which is robust with gain-phase errors for uniform or sparse linear array. In this beamformer, the INC matrix is reconstructed by the estimated steering vector (SV) and the corresponding individual powers of the interference signals, as well as noise power. Firstly, a gain-phase errors model of the sensors is deduced ba

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18

Hu, Ziying, Wei Wang, Fuwang Dong, and Ping Huang. "MIMO Radar Accurate 3-D Imaging and Motion Parameter Estimation for Target with Complex Motions." Sensors 19, no. 18 (2019): 3961. http://dx.doi.org/10.3390/s19183961.

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In this paper, three-dimensional (3-D) multiple-input multiple-output (MIMO) radar accurate localization and imaging method with motion parameter estimation is proposed for targets with complex motions. To characterize the target accurately, a multi-dimensional signal model is established including the parameters on target 3-D position, translation velocity, and rotating angular velocity. For simplicity, the signal model is transformed into three-joint two-dimensional (2-D) parametric models by analyzing the motion characteristics. Then a gridless method based on atomic norm optimization is pr

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19

Peng, Li, Wang, et al. "SPICE-Based SAR Tomography Over Forest Areas Using a Small Number of P-band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines." Remote Sensing 11, no. 8 (2019): 975. http://dx.doi.org/10.3390/rs11080975.

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Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet an

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20

Martín-del-Campo-Becerra, Gustavo Daniel, Andreas Reigber, Matteo Nannini, and Scott Hensley. "Single-Look SAR Tomography of Urban Areas." Remote Sensing 12, no. 16 (2020): 2555. http://dx.doi.org/10.3390/rs12162555.

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Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduc

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21

Yuan, Sihan, and Daniel J. Eisenstein. "Decorrelating the errors of the galaxy correlation function with compact transformation matrices." Monthly Notices of the Royal Astronomical Society 486, no. 1 (2019): 708–24. http://dx.doi.org/10.1093/mnras/stz899.

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Abstract Covariance matrix estimation is a persistent challenge for cosmology, often requiring a large number of synthetic mock catalogues. The off-diagonal components of the covariance matrix also make it difficult to show representative error bars on the 2-point correlation function (2PCF) since errors computed from the diagonal values of the covariance matrix greatly underestimate the uncertainties. We develop a routine for decorrelating the projected and anisotropic 2PCF with simple and scale-compact transformations on the 2PCF. These transformation matrices are modelled after the Cholesky

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22

Guivant, Jose, Karan Narula, Jonghyuk Kim, Xuesong Li, and Subhan Khan. "Compressed Gaussian Estimation under Low Precision Numerical Representation." Sensors 23, no. 14 (2023): 6406. http://dx.doi.org/10.3390/s23146406.

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This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to i

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23

Ringh, Axel, Johan Karlsson, and Anders Lindquist. "Multidimensional Rational Covariance Extension with Applications to Spectral Estimation and Image Compression." SIAM Journal on Control and Optimization 54, no. 4 (2016): 1950–82. http://dx.doi.org/10.1137/15m1043236.

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24

Peyrega, Charles, Dominique Jeulin, Christine Delisée, and Jérôme Malvestio. "3D MORPHOLOGICAL MODELLING OF A RANDOM FIBROUS NETWORK." Image Analysis & Stereology 28, no. 3 (2011): 129. http://dx.doi.org/10.5566/ias.v28.p129-141.

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In the framework of the Silent Wall ANR project, the CMM and the US2B are associated in order to characterize and to model fibrous media studying 3D images acquired with an X-Ray tomograph used by the US2B. The device can make 3D images of maximal 23043 voxels with resolutions in the range of 2 μm to 15 μm. Using mathematical morphology, measurements on the 3D X-Ray CT images are used to characterize materials. For example measuring the covariance on these images of an acoustic insulating material made of wooden fibres highlights the isotropy of the fibres orientations in the longitudinal plan

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25

Wang, Lanmei, Yao Wang, Guibao Wang, and Jianke Jia. "Near-field sound source localization using principal component analysis–multi-output support vector regression." International Journal of Distributed Sensor Networks 16, no. 4 (2020): 155014772091640. http://dx.doi.org/10.1177/1550147720916405.

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In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the sign

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26

Zeng, Huixian, and Jinguang Zeng. "Research on Black-Litterman Index Enhancement Strategy——Based on the Ledoit-Wolf Compression Estimation Method to Optimize the CSI 500 Index Enhancement Strategy." International Business Research 15, no. 2 (2022): 60. http://dx.doi.org/10.5539/ibr.v15n2p60.

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Financial risks may often lead to significant losses. A reasonable capital management model can prevent financial risks and enhance financial services to the real economy. The Black-Litterman model can reduce risks through asset allocation. This paper uses the Black-Litterman model to construct an enhanced strategy applied to the CSI 500 Index, and selects the backtest from December 1, 2019 to December 1, 2021. Through the strategy backtest, it can be found that: whether it is considered or not Transaction costs, using analysts&rsquo; consensus target price as the input point of view of th

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Dai, Shuxian, Yujin Zhang, Wanqing Song, Fei Wu, and Lijun Zhang. "Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis." Electronics 8, no. 12 (2019): 1431. http://dx.doi.org/10.3390/electronics8121431.

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Image rotation is a common auxiliary method of image tampering, which can make the forged image more realistic from the geometric perspective. Most algorithms of image rotation angle estimation employ the peak value on the Fourier spectrum; however, JPEG post-processing brings additional peak interferences to the spectrum, which has a great impact on algorithm performance. In this paper, angle estimation is carried out for images compressed by JPEG. Firstly, the Fourier cyclic spectrum of image covariance is calculated, followed by semi-soft threshold wavelet transform to eliminate the block a

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28

Ghalenoei, Emad, Jan Dettmer, Mohammed Y. Ali, and Jeong Woo Kim. "Trans-dimensional gravity and magnetic joint inversion for 3-D earth models." Geophysical Journal International 230, no. 1 (2022): 363–76. http://dx.doi.org/10.1093/gji/ggac083.

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SUMMARY Studying 3-D subsurface structure based on spatial data is an important application for geophysical inversions. However, major limitations exist for conventional regularized inversion when applied to potential-field data. For example, global regularization parameters can mask model features that may be important for interpretation. In addition, 3-D inversions are typically based on data acquired in 2-D at the Earth’s surface. Such data may contain significant spatial error correlations in 2-D due to the choice of spatial sampling, acquisition geometry, ambient noise and model assumptio

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Parbhoo, Sonali, Mario Wieser, Aleksander Wieczorek, and Volker Roth. "Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates." Entropy 22, no. 4 (2020): 389. http://dx.doi.org/10.3390/e22040389.

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Estimating the effects of an intervention from high-dimensional observational data is a challenging problem due to the existence of confounding. The task is often further complicated in healthcare applications where a set of observations may be entirely missing for certain patients at test time, thereby prohibiting accurate inference. In this paper, we address this issue using an approach based on the information bottleneck to reason about the effects of interventions. To this end, we first train an information bottleneck to perform a low-dimensional compression of covariates by explicitly con

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30

Tompkins, Michael J., Juan L. Fernández Martínez, David L. Alumbaugh, and Tapan Mukerji. "Scalable uncertainty estimation for nonlinear inverse problems using parameter reduction, constraint mapping, and geometric sampling: Marine controlled-source electromagnetic examples." GEOPHYSICS 76, no. 4 (2011): F263—F281. http://dx.doi.org/10.1190/1.3581355.

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We have developed a new uncertainty estimation method that accounts for nonlinearity inherent in most geophysical problems, allows for the explicit search of model posterior space, is scalable, and maintains computational efficiencies on the order of deterministic inverse solutions. We accomplish this by combining an efficient parameter reduction technique, a parameter constraint mapping routine, a sparse geometric sampling scheme, and an efficient forward solver. In order to reduce our model domain and determine an independent basis, we implement both a typical principal component analysis, w

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31

Angielczyk, Kenneth D., and H. David Sheets. "Investigation of simulated tectonic deformation in fossils using geometric morphometrics." Paleobiology 33, no. 1 (2007): 125–48. http://dx.doi.org/10.1666/06007.1.

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Tectonic deformation is an important part of the taphonomic histories of many fossils. Although the effects of deformation, and methods to remove those effects, have been a subject of inquiry for over a century, systematic testing under known parameters has never been used to determine how the effects of deformation and the performance of retrodeformation techniques might vary. Comparative studies of morphology depend on the accurate estimation of variance-covariance structure, so an understanding of the effects of retrodeformation on covariance structure is important in assessing the utility

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32

Harbrecht, Helmut, Lukas Herrmann, Kristin Kirchner, and Christoph Schwab. "Multilevel approximation of Gaussian random fields: Covariance compression, estimation, and spatial prediction." Advances in Computational Mathematics 50, no. 5 (2024). http://dx.doi.org/10.1007/s10444-024-10187-8.

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AbstractThe distribution of centered Gaussian random fields (GRFs) indexed by compacta such as smooth, bounded Euclidean domains or smooth, compact and orientable manifolds is determined by their covariance operators. We consider centered GRFs given as variational solutions to coloring operator equations driven by spatial white noise, with an elliptic self-adjoint pseudodifferential coloring operator from the Hörmander class. This includes the Matérn class of GRFs as a special case. Using biorthogonal multiresolution analyses on the manifold, we prove that the precision and covariance operator

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33

"On-grid Adaptive Compressive Sensing Framework for Underdetermined DOA Estimation by Employing Singular Value Decomposition." International Journal of Innovative Technology and Exploring Engineering 8, no. 11 (2019): 3076–82. http://dx.doi.org/10.35940/ijitee.k2433.0981119.

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In the field of Array Signal Processing, the problem of Direction of Arrival (DOA) estimation has attracted colossal attention of researchers in the past few years. The problem refers to estimating the angle of arrival of the incoming signals at the receiver end, from the knowledge of the received signal itself. Generally, an array of antenna/sensors is employed at the receiver for this purpose. In over-determined DOA estimation, the number of signal sources, whose direction needs to be estimated are usually lesser than half the number of antenna array elements, whereas the challenge is to est

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34

Homer, J., O. Friedrich, and D. Gruen. "Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)." Astronomy & Astrophysics, June 11, 2025. https://doi.org/10.1051/0004-6361/202453339.

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Making inferences about physical properties of the Universe requires knowledge of the data likelihood. A Gaussian distribution is commonly assumed for the uncertainties with a covariance matrix estimated from a set of simulations. The noise in such covariance estimates causes two problems: it distorts the width of the parameter contours, and it adds scatter to the location of those contours that is not captured by the widths themselves. For non-Gaussian likelihoods, an approximation may be derived via simulation-based inference (SBI). It is often implicitly assumed that parameter constraints f

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35

Chen, Sihan, Sameh Abdulah, Ying Sun, and Marc G. Genton. "On the impact of spatial covariance matrix ordering on tile low‐rank estimation of Matérn parameters." Environmetrics, June 21, 2024. http://dx.doi.org/10.1002/env.2868.

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AbstractSpatial statistical modeling involves processing an symmetric positive definite covariance matrix, where denotes the number of locations. However, when is large, processing this covariance matrix using traditional methods becomes prohibitive. Thus, coupling parallel processing with approximation can be an elegant solution by relying on parallel solvers that deal with the matrix as a set of small tiles instead of the full structure. The approximation can also be performed at the tile level for better compression and faster execution. The tile low‐rank (TLR) approximation has recently be

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36

Lai, Yan, Cullan Howlett, and Tamara M. Davis. "Faster cosmological analysis with power spectrum without simulations." Monthly Notices of the Royal Astronomical Society, April 30, 2024. http://dx.doi.org/10.1093/mnras/stae1134.

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Abstract Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the Cosmic Microwave Background. However, the inclusion of multiple overlapping tracers, redshift bins, and more non-linear scales means that generating the necessary ensemble of simulations for model-fitting presents a computational burden. In this work, we combine full-shape fitting of galaxy power spectra, analytical covariance matrix estimates, the MOPED (Massively Optimised Parameter Estimation and Data compression) method, and the Taylor expansion interpolation

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Lochrie, Gabrielle, Weichen Chen, and Yongsoon Yoon. "Anti-Windup Adaptive Look-Up Table Algorithms with Application to Data-Driven Engine Controls." Journal of Dynamic Systems, Measurement and Control, June 3, 2024, 1–11. http://dx.doi.org/10.1115/1.4065646.

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Abstract This technical brief presents anti-windup adaptation algorithms for a look-up table, widely used in data-driven engine control systems to accurately model complex features while minimizing computational demand. Engine control systems are prone to uncertain variations due to aging, faults, and manufacturing tolerances, which can impact performance and emissions unless effectively managed. Therefore, there is a growing demand for adaptive features in these systems to maintain robust performance and emissions over their lifespan. This study develops computationally efficient adaptive loo

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Wang, Xiao. "Research on Intelligent Cultivation of College Counselors’ Core Literacy Driven by Big Data." Applied Mathematics and Nonlinear Sciences 9, no. 1 (2024). http://dx.doi.org/10.2478/amns-2024-2684.

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Abstract The cultivation of counselors’ core literacy under the background of big data networks is the inheritance and development of the traditional core literacy ability based on the development of network technology. This paper focuses on the construction of a scientific and reasonable core literacy evaluation system for college counselors in the core literacy cultivation path. For the factor analysis model under the cluster data component form structure, expressed in the form of a diagonal matrix. After disassembling the covariance matrix and other steps, the parameter estimation of the fa

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