LDnADMM-Net: A Denoising Unfolded Deep Neural Network for Direction-of-Arrival Estimations in A Low Signal-to-Noise Ratio

Author:

Liang Can123ORCID,Liu Mingxuan1234,Li Yang1234,Wang Yanhua12345ORCID,Hu Xueyao1234

Affiliation:

1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

2. Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Beijing 100081, China

3. Electromagnetic Sensing Research Center of CEMEE State Key Laboratory, Beijing Institute of Technology, Beijing 100081, China

4. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401120, China

5. Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China

Abstract

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which are hard to fine-tune. Additionally, these methods lack robustness under strong noise. To address these issues, this paper proposes a novel DOA estimation approach using a deep neural network (DNN) for a NULA in a low SNR. The proposed network is designed based on the denoising convolutional neural network (DnCNN) and the alternating direction method of multipliers (ADMM), which is dubbed as LDnADMM-Net. First, we construct an unfolded DNN architecture that mimics the behavior of the iterative processing of an ADMM. In this way, the parameters of an ADMM can be transformed into the network weights, and thus we can adaptively optimize these parameters through network training. Then, we employ the DnCNN to develop a denoising module (DnM) and integrate it into the unfolded DNN. Using this DnM, we can enhance the anti-noise ability of the proposed network and obtain a robust DOA estimation in a low SNR. The simulation and experimental results show that the proposed LDnADMM-Net can obtain high-accuracy and super-resolution DOA estimations for a NULA with strong robustness in a low signal-to-noise ratio (SNR).

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Shandong Provincial Natural Science Foundation

Publisher

MDPI AG

Reference46 articles.

1. A Sparse Uniform Linear Array DOA Estimation Algorithm for FMCW Radar;Xu;IEEE Signal Process. Lett.,2023

2. Zhang, J., Chu, P., and Liao, B. (2023). DOA Estimation in Impulsive Noise Based on FISTA Algorithm. Remote Sens., 15.

3. Super-Resolution DOA Estimation for Wideband Signals Using Non-Uniform Linear Arrays with No Focusing Matrix;Jirhandeh;IEEE Wirel. Commun. Lett.,2022

4. Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries;McClellan;IEEE Signal Process. Lett.,2019

5. A Novel DOA Estimation for Low-Elevation Target Method Based on Multiscattering Center Equivalent Model;Ma;IEEE Geosci. Remote Sens. Lett.,2023

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3