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Efficient DOA Estimation Based on Coprime Array Interpolation With Deep Unfolding Network
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DOI：10.1109/LSP.2026.3698380.png)
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Abstract
En 中文
Coprime arrays increase the degrees of freedom for direction of arrival (DOA) estimation, but virtual-array gaps require costly filling procedures. In this letter, a new DOA estimation method is proposed for coprime arrays based on interpolation and a deep unfolding network. The reconstruction of the interpolated virtual array covariance matrix is formulated as a rank minimization problem and solved using an ADMM-based deep unfolding network with stage-wise learnable parameters and an unsupervised loss inspired by ADMM convergence criteria. Finally, root-MUSIC is employed for DOA estimation. Simulations demonstrate the effectiveness of the proposed method in terms of both computational efficiency and estimation performance.
Keywords:
Coprime array
direction of arrival (DOA)
deep unfolding network
interpolation
Journal
I
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3.9
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583
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0