A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

2017 ◽  
Vol E100.A (12) ◽  
pp. 3049-3053 ◽  
Author(s):  
Di YAO ◽  
Xin ZHANG ◽  
Qiang YANG ◽  
Weibo DENG
Keyword(s):  
Compressed Sensing ◽  
Least Squares ◽  
Adaptive Beamforming ◽  
Total Least Squares ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive ◽  
Beamforming Algorithm

scholarly journals An optimized 2D-Robust Adaptive Beamforming algorithm based on Matrix Completion in sparse array

2018 ◽  
Vol 208 ◽  
pp. 01003
Author(s):  
Jiaying Di ◽  
Wen Hu ◽  
Mengxia Li ◽  
Hongtao Li
Keyword(s):  
Matrix Completion ◽  
Adaptive Beamforming ◽  
Singular Value ◽  
Spectral Norm ◽  
Sparse Array ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive ◽  
Singular Value Thresholding ◽  
Beamforming Algorithm ◽  
Norm Error

The sparse arrays can reduce the cost of beamforming, it greatly reduces the number of actual array elements. However, it also brings about the problem of information loss. A 2D-robust adaptive beamforming algorithm in sparse array based on Singular Value Thresholding algorithm is proposed. At first, a signal model of planar array is established based on Matrix Completion, which can be proved to meet Null Space Property. Then the Genetic Algorithm is used to optimize the sparse array, which is determined to reduce the Spectral Norm Error of Matrix Completion and make the array recovered closer to the full array. In the case of sparse array, the missing information is restored by using the theory of Singular Value Thresholding, and then the restored signal is used to design the digital beamformer weights. This algorithm significantly reduces the Spectral Norm Error and forms robust adaptive beam.

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