DOA estimation using sparse array with gain-phase error based on a novel atomic norm

Gong, Qishu; Ren, Shiwei; Zhong, Shun‐Shi; Wang, Weijiang

doi:10.1016/j.dsp.2021.103266

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…However, in the real environment, there are many non-ideal factors such as gain and phase error [11], mutual coupling error [12], etc., which lead to the deviation between the actual steering vector and the ideal steering vector of the received signal [13]. This deviation seriously affects the accuracy of DOA estimation [14], and consequently, the performance of the above algorithms is seriously affected [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

Liu,

Yin,

et al. 2024

Electronics

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In this paper, a novel weighted block sparse method based on the signal subspace is proposed to realize the Direction-of-Arrival (DOA) estimation under unknown mutual coupling in the uniform linear array. Firstly, the signal subspace is obtained by decomposing the eigenvalues of the sampling covariance matrix. Then, a block sparse model is established based on the deformation of the product of the mutual coupling matrix and the steering vector. Secondly, a suitable set of weighted coefficients is calculated to enhance sparsity. Finally, the optimization problem is transformed into a second-order cone (SOC) problem and solved. Compared with other algorithms, the simulation results of this paper have better performance on DOA accuracy estimation.

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Section: Introductionmentioning

confidence: 99%

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

Liu,

Yin,

et al. 2024

Electronics

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“…This coherence engenders rank deficiency in the noise-free correlation matrix of the received signals [1], which contrasts with the situation involving incoherent signals, thus rendering high-resolution subspace algorithms [1] originally intended for the latter ineffective in multipath scenarios. Spatial smoothing, as elucidated in [3,4], has become the prevalent approach to DOA estimation for coherent signals. It encompasses segmenting the array into overlapping sub-arrays and averaging [1] their covariance matrices [1] to alleviate the rank deficiency issue.…”

Section: Introductionmentioning

confidence: 99%

2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

Li,

Cai

2024

J. Phys.: Conf. Ser.

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We introduce a novel approach for high-precision estimation of two-dimensional (2-D) Direction-of-Arrival (DOA) under conditions of coherent interference. This study innovatively devises an antenna configuration, termed the Dual-Size Spreading Array, specifically tailored to estimate DOAs in such environments. The method involves integrating a subarray with the primary array to create a Dual-Size Extended Array structure. This integration allows for a transformation of the covariance matrix into a new form, where its rank solely depends on the number of impinging waves. Both the subarray and primary array must have a scale no less than the number of incoming wavefronts. This proposed technique not only amplifies the effective aperture but also significantly reduces the computational intricacy associated with DOA estimation procedures. The efficacy of our methodology has been substantiated through simulation results, which demonstrate that it outperforms the conventional spatial smoothing techniques. Moreover, we provide several practical guidelines designed to enhance estimation accuracy while concurrently minimizing computational demands. In summary, this research presents an advanced solution that effectively addresses challenges in 2D DOA estimation under coherent scenarios, achieving improved performance and computational efficiency compared to existing methods.

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“…However, in practical engineering applications, it is not easy to ensure the existence of calibration sources, so it is not suitable for practical engineering applications. However, self-calibration methods can directly estimate the gain-phase error during array operation without placing the pre-calibration source [25][26][27]. Although these methods usually adopt iterative methods and require a large amount of computation, compared with precalibration methods, it is more likely to be implemented in actual engineering.…”

Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

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To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.

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DOA estimation using sparse array with gain-phase error based on a novel atomic norm

Cited by 8 publications

References 31 publications

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

2-D DOA Estimation of Coherent Signals Using the Dual-Size Spreading Array

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

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