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2D adaptive beamforming algorithm based on matrix completion
Received date: 2014-10-16
Revised date: 2015-01-17
Online published: 2015-01-23
Supported by
National Natural Science Foundation of China (61401204)
The two-dimensional (2D) adaptive beamforming based on matrix completion is considered, and a singular value threshold (SVT) based-eigenvalues decomposition linearly constrained minimum variance (SVT-ELCMV) algorithm is proposed. Firstly, a signal model of two-dimensional adaptive beamforming is established based on the matrix completion. And then, the received signal is proved to satisfy the null space property (NSP). Furthermore, the minimum number of array elements to recover the sparse matrices has been analyzed. Finally, the sparse signal is recovered to full signal by SVT algorithm and an effective beam is formed based on the modified LCMV algorithm. This algorithm overcomes the problem that the average sidelobes increases significantly in sparse array, and it keeps valid in the situation when some elements of the sparse array do not work. Computer simulation shows that the SVT-ELCMV algorithm makes the sparse array have the same beamforming capability with the full array. Moreover, the proposed algorithm can restrain the interference signals effectively, so the superiority of the algorithm is verified.
ZENG Wenhao , ZHU Xiaohua , LI Hongtao , CHEN Cheng , MA Yigeng . 2D adaptive beamforming algorithm based on matrix completion[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016 , 37(5) : 1573 -1579 . DOI: 10.7527/S1000-6893.2015.0027
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