scholarly journals A Generalized 2-D DOA Estimation Method Based on Low-Rank Matrix Reconstruction

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 17407-17414 ◽  
Author(s):  
Xiyan Tian ◽  
Jinhui Lei ◽  
Liufeng Du
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Low Rank ◽  
Matrix Reconstruction ◽  
Rank Matrix ◽  
Low Rank Matrix

DOA Estimation for Antenna Array with Partially-Well Sensors via Low-Rank Matrix Completion

2013 ◽  
Vol 756-759 ◽  
pp. 3977-3981 ◽  
Author(s):  
Hua Xing Yu ◽  
Xiao Fei Zhang ◽  
Jian Feng Li ◽  
De Ben
Keyword(s):  
Missing Values ◽  
Linear Array ◽  
Matrix Completion ◽  
Doa Estimation ◽  
Low Rank ◽  
Uniform Linear Array ◽  
Completion Problem ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix

In this paper, we address the angle estimation problem in linear array with some ill sensors (partially-well sensors), which only work well randomly. The output of the array will miss some values, and this can be regarded as a low-rank matrix completion problem due to the property that the number of sources is smaller than the number of the total sensors. The output of the array, which is corrupted by the missing values and the noise, can be complete via the Optspace method, and then the angles can be estimated according to the complete output. The proposed algorithm works well for the array with some ill sensors; moreover, it is suitable for non-uniform linear array. Simulation results illustrate performance of the algorithm.

Alternating strategies with internal ADMM for low-rank matrix reconstruction

2016 ◽  
Vol 121 ◽  
pp. 153-159 ◽  
Author(s):  
Kezhi Li ◽  
Martin Sundin ◽  
Cristian R. Rojas ◽  
Saikat Chatterjee ◽  
Magnus Jansson
Keyword(s):  
Low Rank ◽  
Matrix Reconstruction ◽  
Rank Matrix ◽  
Low Rank Matrix

scholarly journals Covariance Matrix Reconstruction for Direction Finding with Nested Arrays Using Iterative Reweighted Nuclear Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Weijie Tan ◽  
Xi’an Feng
Keyword(s):  
Covariance Matrix ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Finding ◽  
Nuclear Norm ◽  
Low Rank ◽  
Matrix Reconstruction ◽  
Linear Transform

In this paper, we address the direction finding problem in the background of unknown nonuniform noise with nested array. A novel gridless direction finding method is proposed via the low-rank covariance matrix approximation, which is based on a reweighted nuclear norm optimization. In the proposed method, we first eliminate the noise variance variable by linear transform and utilize the covariance fitting criteria to determine the regularization parameter for insuring robustness. And then we reconstruct the low-rank covariance matrix by iteratively reweighted nuclear norm optimization that imposes the nonconvex penalty. Finally, we exploit the search-free DoA estimation method to perform the parameter estimation. Numerical simulations are carried out to verify the effectiveness of the proposed method. Moreover, results indicate that the proposed method has more accurate DoA estimation in the nonuniform noise and off-grid cases compared with the state-of-the-art DoA estimation algorithm.

scholarly journals Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction

2016 ◽  
Vol 64 (20) ◽  
pp. 5327-5339 ◽  
Author(s):  
Martin Sundin ◽  
Cristian R. Rojas ◽  
Magnus Jansson ◽  
Saikat Chatterjee
Keyword(s):  
Singular Vector ◽  
Low Rank ◽  
Matrix Reconstruction ◽  
Rank Matrix ◽  
Low Rank Matrix
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