A DOA estimation algorithm based on the low computational complexity log-sum sparse recovery

•Research highlight 1To further reduce the computational complexity of the KA-SURE-IR and SURE-IR, this paper proposes a low computational complexity log-sum sparse recovery algorithm to achieves DOA estimation. The realization of the low complexity is realized by designing a new descent direction,...

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Vydáno v:Digital signal processing Ročník 168; s. 105623
Hlavní autoři: Lv, Jihui, Liu, Shuai, Jin, Ming, Yan, Feng-Gang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.01.2026
Témata:
A descent direction
DOA
Log-sum sparse recovery
Low computational complexity
Log-sum sparse recovery
A descent direction
Low computational complexity
DOA
ISSN:1051-2004
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Shrnutí:•Research highlight 1To further reduce the computational complexity of the KA-SURE-IR and SURE-IR, this paper proposes a low computational complexity log-sum sparse recovery algorithm to achieves DOA estimation. The realization of the low complexity is realized by designing a new descent direction, and we derive the designed descent direction into a new mathematical expression. The specific process can refer to the third section of the paper.•Research highlight 2KA-SURE-IR and SURE-IR methods cannot determine the step size and selection range in the process of using gradient algorithm to solve the grid mismatch. However, our proposed algorithm can determine the selection range of step size (Remark 3), which is a relative advantage.•Research highlight 3To make the reader understand the proposed algorithm more clearly, it should be noted that in the process of iterative solving, the proposed algorithm belongs to the on-grid DOA estimation method, which does not use gradient descent to solve the grid mismatch but uses the designed descent direction to achieve sparse signal recovery and DOA estimation. The super-resolution iterative reweighted (SURE-IR) algorithm and the prior-knowledge aided super-resolution iterative reweighted (KA-SURE-IR) algorithm provide an important reference for the research of log-sum sparse recovery. However, even if the matrix inverse lemma is used, SURE-IR and KA-SURE-IR still have the problem of high computational complexity. Therefore, this paper designs a descent direction to achieve low complexity log-sum sparse recovery and direction of arrival (DOA) estimation. Firstly, the received signals are decomposed by singular value decomposition (SVD), and the corresponding log-sum sparse model is established. Then, the log-sum sparse model is relaxed to a convex model, the multiple signal classification (MUSIC) algorithm is used to provide prior information to promote sparse recovery, and the theoretical optimal value of the sparse signals in each iteration calculation is solved. Secondly, a descent direction is designed according to the current value and the theoretical optimal value of the sparse signals in each iteration calculation. Finally, the computational complexity of the proposed algorithm is reduced by selecting the regularization parameters as large as possible to reduce the influence of the residual value and by combining the matrix inverse lemma. The simulation results validated the effectiveness of the proposed algorithm in DOA estimation.
ISSN:1051-2004
DOI:10.1016/j.dsp.2025.105623