Deep unfolded network based on SLIM for direction-of-arrival estimation via nested array

Ninghui Li; Xiaokuan Zhang; Fan Lv; Jiahua Xu; Binquan Dai; Zhaolong Wang; Binfeng Zong; Meng Wu

doi:10.1117/12.3034156

13 June 2024 Deep unfolded network based on SLIM for direction-of-arrival estimation via nested array

Ninghui Li, Xiaokuan Zhang, Fan Lv, Jiahua Xu, Binquan Dai, Zhaolong Wang, Binfeng Zong, Meng Wu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131804X (2024) https://doi.org/10.1117/12.3034156
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

For direction-of-arrival estimation problems, deep learning (DL) has shown excellent performance recently owing to the effectiveness and robustness to complicated cases. However, DL is always requiring massive data and lacks explainable theory, which limits its practical application. Fortunately, deep unfolding is able to overcome the disadvantages of DL and empirically achieves fast convergence. Inspired by that, we construct a deep unfolded network according to the famous Sparse Learning via Iterative Minimization (SLIM), yielding a method called learned-SLIM (LSLIM). LSLIM is able to converge efficiently and inherits the advantages of SLIM, such as low computational complexity, excellent sparsity performance. In addition, nested array is further adopted in LSLIM for high estimation accuracy. Extensive simulations are presented to illustrate the superior of the proposed LSLIM beyond other state-of-the-art algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ninghui Li, Xiaokuan Zhang, Fan Lv, Jiahua Xu, Binquan Dai, Zhaolong Wang, Binfeng Zong, and Meng Wu "Deep unfolded network based on SLIM for direction-of-arrival estimation via nested array", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131804X (13 June 2024); https://doi.org/10.1117/12.3034156

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KEYWORDS

Simulations

Neural networks

Education and training

Matrices

Network architectures

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Error analysis

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