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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 0020 +2 more
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Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems
IEEE Wireless Communications Letters, 2020Multiple signal classification (MUSIC) has been widely applied in wireless communications for direction-of-arrival (DOA) estimation. For massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in transceiver design to reduce the cost of radio frequency chains.
Si Li +5 more
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Low-Cost Adaptive Maximum Entropy Covariance Matrix Reconstruction for Robust Beamforming
2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020In this paper, we present a novel low-complexity adaptive beamforming technique using a conjugate gradient (CG) algorithm to avoid matrix inversions. The proposed method exploits algorithms based on the maximum entropy power spectrum (MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so that the beamforming weights are updated
Saeed Mohammadzadeh +3 more
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Robust adaptive beamforming based on interference covariance matrix sparse reconstruction
Signal Processing, 2014Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in the training data. In this paper, we reconstruct the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance matrix.
Yujie Gu 0001 +3 more
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A Novel Clutter Covariance Matrix Reconstruction Method for Airborne STAP
2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP), 2020The clutter plus noise covariance matrix (CNCM) usually estimated by the training snapshots is the key to obtain the weight vector in space-time adaptive processing (STAP). However, the CNCM is difficult to estimate accurately in small samples, which affects the target estimation seriously.
Mingxin Liu, Lin Zou, Xuegang Wang
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RFI Localization via Generalized Augmented Covariance Matrix Reconstruction
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022Jingyu Tao, Dong Zhu, Fei Hu 0002
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2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017
In this paper, a matrix pencil characteristic equation-based source number estimator is proposed. An enhanced matrix is defined through partition-and-stacking process by the original data on the uniform linear array, and its covariance matrix is computed, which is proved to be a toeplitz conjugate symmetry matrix.
Jing Wang 0124, Fei Ji, Fangjiong Chen
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In this paper, a matrix pencil characteristic equation-based source number estimator is proposed. An enhanced matrix is defined through partition-and-stacking process by the original data on the uniform linear array, and its covariance matrix is computed, which is proved to be a toeplitz conjugate symmetry matrix.
Jing Wang 0124, Fei Ji, Fangjiong Chen
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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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Low-Complexity Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction
2019A new beamformer based on covariance matrix reconstruction is introduced. The essence of the new approach is to eliminate the desired signal component in the sample covariance matrix and thus complex integral operation is avoided in the procession of covariance matrix reconstruction.
Zhiwei Guan +3 more
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