All-optical Computing for Direction-of-arrival Estimation Beyond Diffraction Limits

We propose a super-resolution diffractive neural network (S-DNN) for all-optical DOA estimation, achieving angular resolution beyond the diffraction limit and enabling low-latency, cost-effective integrated sensing and communication.

Authors

Xing Lin (ORCID: https://orcid.org/0000-0003-3170-7554)
Yuan Shen (ORCID: https://orcid.org/0000-0002-9396-1964)
Sheng Gao (ORCID: https://orcid.org/0009-0004-2860-0881)
Hang Chen (ORCID: https://orcid.org/0000-0001-9999-1358)
Haiou Zhang
Zhi Sun

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_at.2025.jss129_8
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

All-optical Computing for Direction-of-arrival Estimation Beyond Diffraction Limits

Xing Lin, Yuan Shen, Sheng Gao, Hang Chen et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

All-optical Computing for Direction-of-arrival Estimation Beyond Diffraction Limits

Xing Lin, Yuan Shen, Sheng Gao, Hang Chen, Haiou Zhang, Zhi Sun

January 1, 2025article en

Abstract

We propose a super-resolution diffractive neural network (S-DNN) for all-optical DOA estimation, achieving angular resolution beyond the diffraction limit and enabling low-latency, cost-effective integrated sensing and communication.

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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All-optical Computing for Direction-of-arrival Estimation Beyond Diffraction Limits — Xing Lin, Yuan Shen, et al. · (2025) | TGRS Research Map | TGRS