Relevant bibliographies by topics / Compressive covariance estimation

Academic literature on the topic 'Compressive covariance estimation'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Compressive covariance estimation"

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Azizyan, Martin, Akshay Krishnamurthy, and Aarti Singh. "Extreme Compressive Sampling for Covariance Estimation." IEEE Transactions on Information Theory 64, no. 12 (December 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 the need for applying the complicated ℓ 1 -minimization otherwise required for the reconstruction of the original signal. Moreover, the signal need not be spectrally sparse. A study of the performance of the power spectral estimator is conducted taking into account the properties of the different bases of the covariance subspace needed for compressive covariance sensing, as well as different linear sparse rulers by which compression is achieved. A method is proposed to benefit from the possible computational efficiency resulting from the use of the Fourier basis of the covariance subspace without considerably affecting the spectrum estimation performance.
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Pourkamali‐Anaraki, Farhad. "Estimation of the sample covariance matrix from compressive measurements." IET Signal Processing 10, no. 9 (December 2016): 1089–95. http://dx.doi.org/10.1049/iet-spr.2016.0169.

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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 offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI) is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA) with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.
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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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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 (February 2019): 318–31. http://dx.doi.org/10.1109/taes.2018.2850459.

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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 proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.
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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 (August 6, 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 paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.
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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 (July 31, 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 estimation of single tones in colored noise using higher order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.
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Book chapters on the topic "Compressive covariance estimation"

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Alwan, Nuha A. S. "Investigation of the Effect of Different Covariance Estimation Methods on the Performance of Least Squares Compressive Covariance Sensing." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200802.

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Compressive covariance sensing (CCS) can recover the second-order statistics of a signal that has undergone compression, and this can be achieved without the requirement of sparsity conditions. Instead, certain structure information in the statistical domain is to be captured during compression. In particular, least squares compressive covariance sensing is considered which requires the estimation of the covariance of the available compressed signal in order to recover the covariance matrix of the original signal. Different covariance estimation methods are applied and the CCS performance compared, in the presence of white Gaussian noise, in terms of the normalized mean square error between the true and recovered covariance.
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Conference papers on the topic "Compressive covariance estimation"

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Testa, Matteo, and Enrico Magli. "Distributed covariance estimation for compressive signal processing." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421217.

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Zhang, Zhe, Zhi Tian, Bingchen Zhang, Wen Hong, Yirong Wu, and Li Li. "Multi-channel SAR covariance matrix estimation based on compressive covariance sensing." In 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). IEEE, 2016. http://dx.doi.org/10.1109/cosera.2016.7745695.

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Qiao, Heng, and Piya Pal. "Stable compressive low rank Toeplitz covariance estimation without regularization." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869065.

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Díaz, Elkin, Jonathan Monsalve, Andrés Guerrero, and Henry Arguello. "Covariance Matrix Estimation from Multiple Subsets in Compressive Spectral Imaging." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cosi.2018.ctu5d.1.

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Monsalve, Jonathan, Miguel Marquez, Karen Sanchez, Carlos Hinojosa, Inaki Esnaola, and Henry Arguello. "Cocosvi: Single Snapshot Compressive Spectral Video Via Covariance Matrix Estimation." In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2022. http://dx.doi.org/10.1109/whispers56178.2022.9955136.

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Li, Yuanxin, and Yuejie Chi. "Compressive parameter estimation with multiple measurement vectors via structured low-rank covariance estimation." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884656.

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Monsalve, Jonathan, Miguel Marquez, Inaki Esnaola, and Henry Arguello. "Compressive Covariance Matrix Estimation from a Dual-Dispersive Coded Aperture Spectral Imager." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506077.

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Blanco, Geison, Juan Perez, Jonathan Monsalve, Miguel Marquez, Inaki Esnaola, and Henry Arguello. "Single Snapshot System for Compressive Covariance Matrix Estimation for Hyperspectral Imaging via Lenslet Array." In 2021 XXIII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). IEEE, 2021. http://dx.doi.org/10.1109/stsiva53688.2021.9592019.

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