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 Zehua Dai,Liang Zhang,Xiao Han,et al.An Off-grid DOA Estimation Method for Passive Sonar Detection Based on Iterative Proximal Projection[J].Journal of Marine Science and Application,2024,(2):417-424.[doi:10.1007/s11804-024-00419-0]
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An Off-grid DOA Estimation Method for Passive Sonar Detection Based on Iterative Proximal Projection


An Off-grid DOA Estimation Method for Passive Sonar Detection Based on Iterative Proximal Projection
Zehua Dai123 Liang Zhang123 Xiao Han123 Jingwei Yin123
Zehua Dai123 Liang Zhang123 Xiao Han123 Jingwei Yin123
1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
2 Key Laboratory for Polar Acoustics and Application of Ministry of Education, Harbin Engineering University, Harbin 150001, China;
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
DOA estimation|Sparse reconstruction|Off-grid model|Iterative proximal projection|Passive sonar detection
Traditional direction of arrival (DOA) estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems. This approach often introduces errors into the sparse representation model, necessitating the development of improved DOA estimation algorithms. Moreover, conventional DOA estimation methods typically assume that the signal coincides with a predetermined grid. However, in reality, this assumption often does not hold true. The likelihood of a signal not aligning precisely with the predefined grid is high, resulting in potential grid mismatch issues for the algorithm. To address the challenges associated with grid mismatch and errors in sparse representation models, this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection (IPP). In the proposed method, we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters. A proximal function optimization model is utilized to address non-convex and nonsmooth sparse representation problems in DOA estimation. Subsequently, we leverage the smoothly clipped absolute deviation penalty (SCAD) function to compute the proximal operator for solving the model. Simulation and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.


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Received date: 2023-10-20;Accepted date: 2023-11-23。
Foundation item: This work is supported by the National Science Foundation for Distinguished Young Scholars (Grant No.62125104) and the National Natural Science Foundation of China (Grant No.52071111).
Corresponding author: Liang Zhang,E-mail:zhangliang19840425@gmail.com
Last Update: 2024-05-28