DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

Wang, Yuexian; Xie, Jian; Trinkle, Matthew; Ng, Brian W.-H.

doi:10.1109/lwc.2019.2903497

Cited by 35 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the mutual coupling characteristics of the array may vary as the environmental conditions change, and therefore the calibration source-based methods are only effective under certain circumstances. Consequently, another class of auto-calibration algorithms is advanced to compensate for the mutual coupling effect without any calibration sources [19][20][21][22][23][24][25]. In [19], an eigenstructure-based algorithm was developed for the DOA estimation and mutual coupling auto-calibration at the same time.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, a fourth order cumulant (FOC) method was also proposed [22], which performs better especially for strong mutual coupling effects. In [23][24][25], the authors utilized sparse reconstruction methods to improve the DOA estimation performance under unknown mutual coupling. However, these methods are computationally demanding due to the l 1 -norm minimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Xie

Wang

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Xie

Wang

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to further exploit the block sparsity of the signal, a reweighted ℓ 1 -norm method [12] is introduced to solve the block sparse DOA estimation with MC, where the weighted matrix is determined by a MUSIC-like spectrum. In [13], a sparsity-inducing method over covariance matrix is proposed, which provides larger degrees of freedom and array aperture. A unified self-calibration framework for DOA estimation in the presence of non-ideal array is proposed in [14] using the sparse Bayesian learning perspective.…”

Section: Introductionmentioning

confidence: 99%

A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

Zhang

Jiang

et al. 2019

IEEE Commun. Lett.

View full text Add to dashboard Cite

In this letter, we consider the direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling in application to uniform linear arrays (ULAs). A novel method is proposed that treats the DOA estimation as a block sparse signal reconstruction problem with a modified array manifold matrix which utilizes the information of entire array output. A block smoothed ℓ 0 norm approximation technique is introduced to solve the problem. Finally, an iterative proximal algorithm is proposed. Simulation results are presented that show the superiority of the proposed method against other known techniques.Index Terms-Direction-of-arrival (DOA), mutual coupling, block sparse reconstruction, block smoothed ℓ 0 (BSL0).

show abstract

“…Chen et al investigate the problem of off-grid DOA estimation with coupling effects, and adopt the p -norm-based technique [14] and the relevance vector machine [15], respectively, to handle different cases, reducing amount of computation while preserving satisfactory estimation performance. Taking advantage of the uncorrelation between signals, Wang et al address the issue from the perspective of group sparsity reconstruction of a long vector with mitigated noise components, and bring about an even better estimation accuracy and resolution [16].The aforementioned work provides the solutions following the second-order statistics (SOS) and is unable to work properly in spatial colored noise if the noise covariance matrix is not available in advance. Since high-order statistics are insensitive to colored noise and can inherently enhance the DOFs [17][18][19], some study has been devoted to the suppression of unknown mutual coupling and colored noise using fourth-order cumulants (FOC) [20][21][22][23][24].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Cumulant-Based DOA Estimation of Noncircular Signals against Unknown Mutual Coupling

Wang

Zheng

2020

Sensors

View full text Add to dashboard Cite

To effectively find the direction of non-circular signals received by a uniform linear array (ULA) in the presence of non-negligible perturbations between array elements, i.e., mutual coupling, in colored noise, a direction of arrival (DOA) estimation approach in the context of high order statistics is proposed in this correspondence. Exploiting the non-circularity hidden behind a certain class of wireless communication signals to build up an augmented cumulant matrix, and carrying out a reformulation of the distorted steering vector to extract the angular information from the unknown mutual coupling, by exploiting the characteristic of mutual coupling, i.e., a limited operating range and an inverse relation of coupling effects to interspace, we develop a MUSIC-like estimator based on the rank-reduction (RARE) technique to directly determine directions of incident signals without mutual coupling compensation. Besides, we provide a solution to the problem of coherency between signals and mutual coupling between sensors co-existing, by selecting a middle sub-array to mitigate the undesirable effects and exploiting the rotation-invariant property to blindly separate the coherent signals into different groups to enhance the degrees of freedom. Compared with the existing robust DOA methods to the unknown mutual coupling under the framework of fourth-order cumulants (FOC), our work takes advantage of the larger virtual array and is able to resolve more signals due to greater degrees of freedom. Additionally, as the effective aperture is virtually extended, the developed estimator can achieve better performance under scenarios with high degree of mutual coupling between two sensors. Simulation results demonstrate the validity and efficiency of the proposed method.

show abstract

DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

Cited by 35 publications

References 12 publications

Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

Cumulant-Based DOA Estimation of Noncircular Signals against Unknown Mutual Coupling

Contact Info

Product

Resources

About