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Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Sensors 2018, 18(5), 1476; https://doi.org/10.3390/s18051476
Received: 9 April 2018 / Revised: 28 April 2018 / Accepted: 4 May 2018 / Published: 8 May 2018
(This article belongs to the Section Sensor Networks)
When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers. View Full-Text
Keywords: covariance matrix reconstruction; robust adaptive beamforming; sensor position errors; weighted subspace fitting (WSF) covariance matrix reconstruction; robust adaptive beamforming; sensor position errors; weighted subspace fitting (WSF)
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MDPI and ACS Style

Chen, P.; Yang, Y.; Wang, Y.; Ma, Y. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction. Sensors 2018, 18, 1476. https://doi.org/10.3390/s18051476

AMA Style

Chen P, Yang Y, Wang Y, Ma Y. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction. Sensors. 2018; 18(5):1476. https://doi.org/10.3390/s18051476

Chicago/Turabian Style

Chen, Peng, Yixin Yang, Yong Wang, and Yuanliang Ma. 2018. "Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction" Sensors 18, no. 5: 1476. https://doi.org/10.3390/s18051476

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