Modified Gram-Schmidt orthogonalization of covariance matrix adaptive beamforming based on data preprocessing

2012 ◽  
Author(s):  
Xiaopeng Yang ◽  
Xiaona Hu ◽  
Yongxu Liu
Keyword(s):  
Covariance Matrix ◽  
Data Preprocessing ◽  
Adaptive Beamforming ◽  
Schmidt Orthogonalization

A Fast Adaptive Beamforming Algorithm Based on Gram-Schmidt Orthogonalization

2021 ◽  
Vol 16 (4) ◽  
pp. 642-650
Author(s):  
Bing-Feng Qian ◽  
Shi-Jie Gao ◽  
Quan-Feng Li ◽  
Qian Zhang ◽  
Ye Wang
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Data Preprocessing ◽  
Adaptive Threshold ◽  
Adaptive Beamforming ◽  
High Signal ◽  
Signal To Noise ◽  
Schmidt Orthogonalization ◽  
Beamforming Algorithm

Fast implementation is one of the important indexes of the ADBF algorithm. The advantages of the Gram-Schmidt (GS) orthogonalization algorithm are that it can reconstruct the interference subspace well under the high signal-to-noise ratio and has fast convergence speed and low computational complexity. This paper studies the RGS algorithm for GS orthogonalization of sampling covariance matrix. To estimate the interference subspace more accurately, this paper modifies the orthogonal adaptive threshold of covariance matrix, and extends the proposed GS orthogonal algorithm of covariance matrix based on data preprocessing to the adaptive beamforming processing at subarray level.

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