DOA estimation based on compressed sensing with gain/phase uncertainties

Hu, Bin; Wu, Xiaochuan; Xin, Zhang; Yang, Qiang; Deng, Weibo

doi:10.1049/iet-rsn.2018.5087

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…OGSBI and SURE IR algorithms refer to the off-grid sparse Bayesian inference [19] and super-resolution iterative reweighted [24], respectively. The yellow squared line is referred to simultaneous orthogonal matching pursuit with total least squares (SOMP TLS) [25]. The CRB curve is calculated with joint DOA and gain-phase estimation.…”

Section: Simulation Results and Rmse Analysismentioning

confidence: 99%

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

Wei¹,

Wang²,

Dong³

et al. 2021

PIER M

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The performance of direction-of-arrival (DOA) estimation algorithms degrades when a partly calibrated array is adopted due to the existing unknown gain-phase uncertainties. In addition, the spatial discretized searching grid also limits the performance improvement and effectiveness of subspacebased DOA estimation algorithms, especially when the true angles do not lie on the grid points which is referred to the off-grid problem alike. In this paper, a self-calibration DOA estimation algorithm is proposed which solves the array calibration and off-grid problems simultaneously. Firstly, the signal model for a partly calibrated array with gain-phase uncertainties is established. To suppress the offgrid errors, an optimization problem for joint parameters estimation is constructed by substituting the approximation of the steering vector into a newly constructed objective function. The alternative minimization (AM) algorithm is employed to calculate the joint DOA and gain-phase uncertainty estimations. Within each iteration step of the optimization problem, a closed-form solution is derived that guarantees the convergence of the proposed algorithm. Furthermore, the Cramér-Rao bound (CRB) for the partly calibrated arrays with unknown gain-phase uncertainties is also derived and analyzed in the paper. Simulation results demonstrate the effectiveness of the proposed algorithm.

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Section: Simulation Results and Rmse Analysismentioning

confidence: 99%

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

Wei¹,

Wang²,

Dong³

et al. 2021

PIER M

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show abstract

“…where F and H are n × n matrices, Z = diag(z), and z is a n × 1 vector. Similar to Equation (15), in Equation (14b) we have [12]…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…Then, the mixed far-field and near-field source localization was concerned in [14] with the CSA under the condition of gain-phase perturbations. A DOA estimation method based on the errors-in-variables model, i.e., the simultaneous orthogonal matching pursuit-total least squares algorithm, was proposed in [15], where the sparsity of the received signal is exploited. By using the sparse representation technique, an optimization method for direction finding with unknown gain-phase in the presence of spatially non-uniform noise was proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error

Guo

Feng

et al. 2022

Remote Sensing

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Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

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“…In the practical antenna array, the gain-phase errors among antennas degrade the DOA estimation performance [28,29]. The CS-based method for the DOA estimation is proposed in [30], and ref. [31] describes the localization method for the near-field sources with gain-phase errors.…”

Section: Introductionmentioning

confidence: 99%

A New Atomic Norm for DOA Estimation With Gain-Phase Errors

Chen

Cao

et al. 2020

IEEE Trans. Signal Process.

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The direction of arrival (DOA) estimation problem has been studied for decades and is essential in the radar, wireless communications, and array signal processing applications. In this paper, the DOA estimation problem in the scenario with gainphase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, an optimization method is formulated via deriving a dual norm of the new atomic norm. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.

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DOA estimation based on compressed sensing with gain/phase uncertainties

Cited by 15 publications

References 21 publications

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error

A New Atomic Norm for DOA Estimation With Gain-Phase Errors

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