The effect of the covariance matrix of ionospherically propagated signals on the choice of direction finding algorithm

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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Noise Suppression for Direction of Arrival Estimation in Co-located MIMO Sonar

Sensors ◽

10.3390/s19061325 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1325 ◽

Cited By ~ 1

Author(s):

Xue Cheng ◽

Yingmin Wang

Keyword(s):

Dimension Reduction ◽

Covariance Matrix ◽

Gaussian Noise ◽

Noise Suppression ◽

Direction Of Arrival ◽

Doa Estimation ◽

Direction Finding ◽

White Gaussian Noise ◽

Multiple Targets ◽

Noise Component

Noise suppression capacity in multiple-input multiple-output (MIMO) sonar signal processing is derived under the assumption of white Gaussian noise. However, underwater noise mainly includes white Gaussian noise and colored noise. There exists a certain correlation between the noise signals received by each MIMO sonar array element. The performance of traditional direction-of-arrival (DOA) estimation methods decreases obviously in complex marine noise. In this paper, we propose a marine environment noise suppression method for MIMO applied to multiple targets’ DOA estimation. The noise field can be decomposed into a symmetric noise component and an asymmetric noise component. We use the covariance matrix imaginary component to pre-estimate the signal sources, then use the dimension reduction transformation to reconstruct the real component of the covariance matrix. The Toeplitz technique is utilized to reduce the correlation of the reconstructed covariance matrix. Thus, the subspace decomposition-based techniques such as multiple signal classification (MUSIC) can be used for multiple targets’ DOA estimation. To reduce the computational complexity of the methods, search-free direction-finding techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) can be utilized. As a result, the proposed methods can achieve better direction-finding performance in the condition of limited snapshots with lower computational cost. The corresponding Cramer-Rao bound (CRB) is deduced and the signal-to-noise ratio (SNR) gain obtained by dimension reduction processing is discussed. Simulation results also show the superiority of the proposed method over the existing methods.

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