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DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
IEEE Signal Processing Letters, 2021In this paper, we discuss a new approach to direction of arrival estimation for systems with subarray sampling. We propose to estimate the covariance matrix of the full array from the sample covariance matrices of the subarrays using a neural network. This technique enables the estimation of more sources than radio frequency chains by applying a MUSIC ...
Andreas Barthelme, Wolfgang Utschick
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Focusing-Based Wideband Adaptive Beamforming Using Covariance Matrix Reconstruction
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Most focusing-based beamforming methods are devoted to solely focusing matrix designing, which aims to minimize the overall focusing error. However, these methods may suffer from performance degradation when steering vector (SV) error exists. To maximize the overall performance of focusing-based beamformer, this paper presents an adaptive focusing ...
Peng Chen, Wei Wang, Jingjie Gao
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Covariance Matrix Reconstruction of Nonclassical Light Generated On-Chip
Conference on Lasers and Electro-Optics, 2022We reconstruct covariance matrices of two-mode states generated in an above-threshold on-chip optical parametric oscillator. Up to 2.3 dB squeezing is directly observed and all quadratures are measured, as a function of pump intensity.
Roger A. Kögler +7 more
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Robust adaptive beamforming using interference covariance matrix reconstruction
2016 CIE International Conference on Radar (RADAR), 2016The performance of adaptive beamforming degrades severely when the strong desired signal is present in training snapshots with model mismatch. A robust adaptive beamforming is proposed using interference covariance matrix reconstruction in this paper. In the proposed method, the eigenvalue and eigenvector of desired signal is determined by calculating ...
Xueyao Hu +5 more
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Robust MVDR beamforming based on covariance matrix reconstruction
Science China Information Sciences, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mu, Pengcheng +3 more
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A robust MVDR beamforming based on covariance matrix reconstruction
SPIE Proceedings, 2011The minimum variance distortionless response (MVDR) beamformer has better resolution and much better interference rejection capability than the data-independent beamformers. However, the former is much more sensitive to errors, such as the array steering errors caused by direction of arrival mismatch or imprecise sensor calibrations or any other ...
Pengcheng Mu, Dan Li, Qinye Yin
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Linear Prediction-Based Covariance Matrix Reconstruction for Robust Adaptive Beamforming
IEEE Signal Processing Letters, 2021In this letter, a novel reconstruction-based adaptive beamformer is proposed, which uses linear prediction to generate virtual sensor data and extend array aperture. To overcome suppression failure of reconstruction-based adaptive beamformer, a double-side array extending algorithm is proposed for uniform linear array, where the virtual array data can ...
Peng Chen, Jingjie Gao, Wei Wang
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Robust Adaptive Beamforming based on Calibrated Covariance Matrix Reconstruction
Proceedings of the 2017 2nd International Conference on Communication and Information Systems, 2017Adaptive beamformers will suffer performance degradation when a model mismatch exists. For the beamformers based on interference-plus-noise covariance matrix (INCM) reconstruction, the random sensor position perturbation will result in a poor output signal-to-noise-plus-interference ratio (SINR).
Bo Liankun, Xiong Jinyu, Liu Chengyuan
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Journal of Applied Remote Sensing, 2016
In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first.
Yasen Wang, Qinglong Bao, Zengping Chen
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In order to suppress multiple mainlobe interferences and sidelobe interferences simultaneously, a mainlobe interference suppression algorithm is proposed. In this algorithm, the number of mainlobe interferences is estimated through a matrix filter at first.
Yasen Wang, Qinglong Bao, Zengping Chen
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