A Novel Online Mutual Coupling Compensation Algorithm for Uniform and Linear Arrays

Sellone, F.; Serra, Angelo

doi:10.1109/tsp.2006.885732

Cited by 166 publications

(118 citation statements)

References 22 publications

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“…To counteract mutual coupling, the standard approach is to estimate mutual coupling and source profiles based on the received data and particular mutual coupling models [5,[7][8][9][10]12]. For instance, BouDaher et al considered DOA estimation with coprime arrays in the presence of mutual coupling [12].…”

Section: Mutual Couplingmentioning

confidence: 99%

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Super nested arrays: Sparse arrays with less mutual coupling than nested arrays

Liu

Vaidyanathan

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With N denoting the number of sensors, these sparse arrays offer O(N 2 ) freedoms for source estimation because their difference coarrays have O(N 2 )-long ULA segments. These arrays have different shortcomings: coprime arrays have holes in the coarray, MRAs have no closed-form expressions, and nested arrays have relatively large mutual coupling. This paper introduces a new array called the super nested array, which has all the good properties of the nested array, and at the same time reduces mutual coupling significantly. For fixed N , the super nested array has the same physical aperture, and the same hole-free coarray as does the nested array. But the number of sensor pairs with separation λ/2 is significantly reduced. Many theoretical properties are proved and simulations are included to demonstrate the superior performance of these arrays.

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Section: Mutual Couplingmentioning

confidence: 99%

“…This has an adverse effect on the estimation of parameters (e.g., DOA). State-of-the-art approaches aim to decouple (or "remove") the effect of mutual coupling from the received data by using proper mutual coupling models [5][6][7][8][9][10][11][12]. Such methods are usually computationally expensive, and sensitive to model mismatch.…”

Section: Introductionmentioning

confidence: 99%

Super nested arrays: Sparse arrays with less mutual coupling than nested arrays

Liu

Vaidyanathan

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Based on the necessary condition for a unique solution [8], we set P = 3 in this case, i.e., the mutual coefficients with the sensors which are 3d and more apart are neglected. The values of the coefficients are identical to those in Case 1.…”

Section: Case 1)mentioning

confidence: 99%

“…By exploiting the special structure of the mutual coupling matrix, a self-calibration method for uniform circular array (UCA) was proposed in [6] and [7], respectively. Recently, Sellone and Serra presented a novel online mutual coupling compensation algorithm for ULA [8]. This method performs an alternating minimization procedure to compensate mutual coupling of the ULA array.…”

Section: Introductionmentioning

confidence: 99%

Localization of Narrow Band Sources in the Presence of Mutual Coupling via Sparse Solution Finding

Zhang¹,

Wan²,

Huang³

2008

PIER

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Abstract-Making use of the Toeplitz structure of the mutual coupling matrix of the Uniform Linear Array (ULA), estimating the direction-of-arrival (DOA) of the sources as well as the mutual coupling coefficients of the array can be formulated as a linear inverse problem, where the solution is given by the Kronecker product of the vectors with respect to the DOAs and the mutual coupling coefficients. Through mathematical manipulation, these solution vectors can be decoupled. Estimation of the DOAs is cast into the framework of sparse solution finding. To derive the solution, an alternating minimization technique is presented. The proposed method is firstly developed based on the noise free observation covariance matrix, and can be generalized to directly using the snapshots. Using the proposed method, DOA estimation is feasible even in single snapshot case. The performance of the proposed methods with covariance matrix, single snapshot and multiple snapshots are illustrated by computer simulations. Their ability to resolve closely spaced targets and the applicability to correlated sources have also been demonstrated.

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“…These algorithms include the maximum likelihood algorithm [9], the iterative autocalibration method [10], the auxiliary sensor-based methods [11][12][13][14], the cumulant-based method [15], the Rankreduction (RARE)-based calibration methods [16,17], the sparse representation-based methods [18][19][20], and the matrix reconstruction method [21]. However, some of these methods require a set of calibration signals/auxiliary sensors [9,[11][12][13][14] or iterative/high order statistics/nonlinear optimization computations [10,[15][16][17][18][19][20]. Moreover, all such methods are designed for scalar sensor arrays and are not applicable to the vector sensor arrays.…”

Section: Introductionmentioning

confidence: 99%

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

Shu

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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This paper is concerned with DOA estimation using a single-electromagnetic vector sensor in the presence of mutual coupling. Firstly, we apply the temporally smoothing technique to improve the identifiability limit of a single-vector sensor. In particular, we establish sufficient conditions for constructing temporally smoothed matrices to resolve K > 2 incompletely polarized (IP) monochromatic signals with a single-vector sensor. Then, we propose an efficient ESPRIT-based method, which does not require any calibration signals or iterative operations, to jointly estimate the azimuth-elevation angles and the mutual coupling coefficients. Finally, we derive the Cramér-Rao bound (CRB) for the problem under consideration.

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A Novel Online Mutual Coupling Compensation Algorithm for Uniform and Linear Arrays

Cited by 166 publications

References 22 publications

Super nested arrays: Sparse arrays with less mutual coupling than nested arrays

Super nested arrays: Sparse arrays with less mutual coupling than nested arrays

Localization of Narrow Band Sources in the Presence of Mutual Coupling via Sparse Solution Finding

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

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