Robust Direction-of-Arrival Estimation for Coprime Array in the Presence of Miscalibrated Sensors

Kou, Jiaxun; Li, Ming; Jiang, Chunlan

doi:10.1109/access.2020.2971842

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Here, g behaves like the virtual GP error vector. According to Equations (22) and (23), Equation (21) is rewritten as…”

Section: V-focls Methods For Clutter Spectrum and Gp Errors Estimationmentioning

confidence: 99%

“…However, in practical applications, the array model may result from various imperfections due to the artificial and hardware itself effects, which have detrimental effects on the performance of the STAP algorithms. To deal with this problem, a number of self-calibration methods for sparse arrays in the presence of array errors have been proposed [19][20][21][22][23][24][25][26][27][28]. For instance, in [19], a jointly gain and phase (GP) error calibration and direction of arrival (DOA) estimation approach for nested arrays was proposed by employing the partial Toeplitz structure of the covariance matrix and the sparse total least-squared (LS) methods.…”

Section: Introductionmentioning

confidence: 99%

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Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

Yang

Huang

2020

IET Radar Sonar & Navi

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Space‐time adaptive processing (STAP) for sparse arrays such as coprime and nested arrays is shown to have improved performance for clutter suppression in airborne radar as compared with uniform linear arrays with the same size. However, most of the existing STAP algorithms are derived based on the assumption that the array manifold is exactly known. In this study, a robust STAP algorithm for the clutter suppression with coprime arrays in the presence of gain and phase (GP) errors is proposed. In particular, the virtual space‐time signal with GP errors is first formulated using the covariance matrix of the perturbed array received data under the Gaussian distributed interference assumption. Then, the problem of the clutter spectrum and GP errors' estimates is formulated as a joint minimization optimization problem by exploiting the sparsity of the clutter in the angle–Doppler plane. Finally, a two‐step approach based on the sparse recovery and least‐squared method is developed to solve the resultant optimization problem and the STAP filter can be designed using the clutter spectrum and GP errors' estimates. Compared with the existing sparsity‐based STAP algorithms for coprime arrays, the proposed algorithm shows more robustness to GP errors as illustrated by simulation results.

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“…Here, g behaves like the virtual GP error vector. According to Equations (22) and (23), Equation (21) is rewritten as…”

Section: V-focls Methods For Clutter Spectrum and Gp Errors Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

Yang

Huang

2020

IET Radar Sonar & Navi

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“…In this situation, the traditional ULAbased DOA estimation methods are not available. To overcome this challenge, a common solution is to ditch the discontinuous elements and select only the largest contiguous virtual sensors in S v for subsequent processing [17]. However, this method suffers from performance degradation since the elements offered by S v cannot be fully utilized.…”

Section: Array Interpolation and Matrix Recoverymentioning

confidence: 99%

DOA Estimation under GNSS Spoofing Attacks Using a Coprime Array: From a Sparse Reconstruction Viewpoint

Zhao

Shen

et al. 2022

Remote Sensing

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The antispoofing method using the direction-of-arrival (DOA) feature can effectively improve the application security of the global navigation satellite system (GNSS) receivers. In this paper, a sparse reconstruction approach based on a coprime array of antennas is proposed to provide reliable DOA estimation under a GNSS spoofing attack. Specifically, the self-coherence property of genuine satellite signals and spoofing was fully exploited to construct a denoised covariance matrix that enables DOA estimation before receiver despreading. Based on this, an equivalent uniform linear array (ULA) was generated from the constructed covariance matrix via virtual array interpolation. By applying the ideal of sparse reconstruction to an equivalent ULA signal, the preliminary DOA estimation results could be obtained without the need for a number of signals. Considering that the sparse estimation technique suffers from basis mismatch effects, we designed an optimization problem with respect to off-grid error to compensate the initial DOA such that the performance loss of DOA estimation could be reduced. Numerical examples demonstrated the advantages of the proposed method in terms of degrees-of-freedom (DOFs), resolution and accuracy.

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“…We utilized some prior conditions, the sparsity of scattering points in space and the MIMO array manifold, to establish a new sparse imaging method in this paper. Besides, there is limited research on MIMO radar imaging in the presence of outliers, and in practice, this can be caused by radio interference, miscalibrated sensors, and other aspects of the MIMO radar system [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Robust Sparse Imaging Algorithm Using Joint MIMO Array Manifold and Array Channel Outliers

Ding

Wang

et al. 2022

Remote Sensing

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The multiple-input multiple-output (MIMO) radar imaging technology has attracted many scholars due to its many inherent advantages, such as avoiding complex motion compensation and imaging a quickly maneuvering target, compared to inverse synthetic aperture radar (ISAR) imaging. Although some imaging algorithms, such as the 2D fast iterative shrinkage thresholding algorithm (2D-FISTA), can meet the demand for super-resolution, they are not directly suited to MIMO radar imaging, for which the MIMO manifold needs to be considered. In this paper, based on the above questions, we propose the MIMO radar imaging algorithm, utilizing the sparsity of the scattering map in space and the MIMO array manifold, even achieving a good performance in the presence of MIMO channel error. The sparse reconstruction algorithm is developed with the alternative direction method of multipliers (ADMM) with the help of 2D-FISTA and the lp-norm. Then, two algorithms are derived: one is the exact sparse recovery algorithm, and the other is the inexact sparse recovery algorithm. Although the exact sparse recovery algorithm can converge to a more accurate precision than the inexact algorithm, the latter can converge at a faster speed. Finally, the results on simulation data validated the effectiveness of the algorithm.

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Robust Direction-of-Arrival Estimation for Coprime Array in the Presence of Miscalibrated Sensors

Cited by 7 publications

References 27 publications

Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

DOA Estimation under GNSS Spoofing Attacks Using a Coprime Array: From a Sparse Reconstruction Viewpoint

A Robust Sparse Imaging Algorithm Using Joint MIMO Array Manifold and Array Channel Outliers

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