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Adaptive Beamforming Based on Compressed Sensing with Gain/Phase Uncertainties
Bin HU Xiaochuan WU Xin ZHANG Qiang YANG Di YAO Weibo DENG
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E101A
No.8
pp.12571262 Publication Date: 2018/08/01
Online ISSN: 17451337
DOI: 10.1587/transfun.E101.A.1257
Type of Manuscript: LETTER Category: Digital Signal Processing Keyword: digital beamforming, compressed sensing, gain/phase uncertainties, dictionary optimization, total least squares algorithm,
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Summary:
A new method for adaptive digital beamforming technique with compressed sensing (CS) for sparse receiving arrays with gain/phase uncertainties is presented. Because of the sparsity of the arriving signals, CS theory can be adopted to sample and recover receiving signals with less data. But due to the existence of the gain/phase uncertainties, the sparse representation of the signal is not optimal. In order to eliminating the influence of the gain/phase uncertainties to the sparse representation, most present study focus on calibrating the gain/phase uncertainties first. To overcome the effect of the gain/phase uncertainties, a new dictionary optimization method based on the total least squares (TLS) algorithm is proposed in this paper. We transfer the array signal receiving model with the gain/phase uncertainties into an EIV model, treating the gain/phase uncertainties effect as an additive error matrix. The method we proposed in this paper reconstructs the data by estimating the sparse coefficients using CS signal reconstruction algorithm and using TLS method toupdate error matrix with gain/phase uncertainties. Simulation results show that the sparse regularized total least squares algorithm can recover the receiving signals better with the effect of gain/phase uncertainties. Then adaptive digital beamforming algorithms are adopted to form antenna beam using the recovered data.

