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Your search keyword '"Tang, Wen-Gen"' showing total 7 results
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7 results on '"Tang, Wen-Gen"'

6. Block Newtonised orthogonal matching pursuit for off‐grid DOA estimation in the presence of unknown mutual coupling.

Author

Tang, Wen‐Gen, Jiang, Hong, and Zhang, Qi

Subjects
COMPRESSED sensing, ORTHOGONAL matching pursuit, SIGNAL processing, COMPUTATIONAL complexity, COMPUTER simulation
Abstract

Mutual coupling (MC) between array elements can lead to severe accuracy degradation in direction‐of‐arrival (DOA) estimation. In this study, an off‐grid compressed sensing (CS) approach for DOA estimation in the presence of unknown MC is proposed using block Newtonised orthogonal matching pursuit (NOMP). First, the model of DOA estimation with MC is block sparsely represented to fully utilise the whole array aperture. Then, a block NOMP algorithm with multiple measurement vectors (MMV) is developed to achieve the estimates of DOA, in which the Newton refinement procedure is presented by deriving the first‐ and second‐order derivatives of the block steering matrix with respect to the angle grid. Compared with the subspace‐based and sparse recovery‐based methods with MC, the proposed block NOMP approach has robustness in a small number of snapshots and owns no grid‐mismatch effect. Meanwhile, in comparison to several existing off‐grid DOA estimation methods with MC, it can achieve higher estimation accuracy and similarly low computational complexity. The superior performance of the proposed approach is shown via numerical simulations. [ABSTRACT FROM AUTHOR]

Published
2021
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7. Off-Grid DOA Estimation With Mutual Coupling via Block Log-Sum Minimization and Iterative Gradient Descent

Author

Tang, Wen-Gen, Jiang, Hong, and Zhang, Qi

Abstract

Mutual coupling (MC) effect between antennas can deteriorate the direction of arrival (DOA) estimation performance. Block sparse signal representation (BSSR) is an effective solution to mitigate the MC effect. Nevertheless, continuous angle space has to be discretized into a finite set of grid points, which lead to the grid mismatch. In this letter, we present an iterative block sparsity reconstruction algorithm for off-grid DOA estimation with MC. First, by exploiting the banded symmetric Toeplitz structure of MC matrix, a reduced BSSR model is formulated to preferably mitigate the MC effect. Then, an iterative block sparsity reconstruction algorithm with multiple measurement vectors (MMV) is proposed. Due to the NP-hard property of block $l_{0}$ norm minimization, a block log-sum minimization method is developed to encourage sparsity for such a block sparse recovery problem, and a surrogate objective function is derived via the majorization-minimization approach. Finally, an iterative gradient descent algorithm is explored to refine the DOAs. Simulations verify that the proposed off-grid algorithm has robustness under limited snapshots and coherent sources.

Published
2022
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