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

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.

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Alternating strategies with internal ADMM for low-rank matrix reconstruction

Signal Processing ◽

10.1016/j.sigpro.2015.11.002 ◽

2016 ◽

Vol 121 ◽

pp. 153-159 ◽

Cited By ~ 10

Author(s):

Kezhi Li ◽

Martin Sundin ◽

Cristian R. Rojas ◽

Saikat Chatterjee ◽

Magnus Jansson

Keyword(s):

Low Rank ◽

Matrix Reconstruction ◽

Rank Matrix ◽

Low Rank Matrix

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Low rank matrix recovery for joint array self-calibration and sparse model DoA estimation

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) ◽

10.1109/camsap.2017.8313068 ◽

2017 ◽

Cited By ~ 2

Author(s):

Cheng-Yu Hung ◽

Mostafa Kaveh

Keyword(s):

Doa Estimation ◽

Low Rank ◽

Sparse Model ◽

Matrix Recovery ◽

Self Calibration ◽

Rank Matrix ◽

Low Rank Matrix Recovery ◽

Low Rank Matrix

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

International Journal of Antennas and Propagation ◽

10.1155/2019/7657898 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 6

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.

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