Parallel Photon‐Correlation LiDAR With Robust Noise Resistance Driven by Super‐Bunching Light

ABSTRACT High‐performance single‐photon light detection and ranging (LiDAR) technology with robust noise resistance provides a powerful means for environmental perception in extreme scenarios. Here, we propose a parallel photon‐correlation (PPC) LiDAR driven by super‐bunching light, enabling noise‐resistant parallel detection in the single‐photon‐counting regime. Using super‐bunching light with g (2) (0) = 177, we demonstrate robust photon‐correlation imaging at a signal‐to‐background ratio (SBR) of 1:2750 in the 2D parallel imaging experiment, and also achieve noise‐resistant three‐dimensional (3D) target reconstruction. Leveraging the correlation between the reference and echo signals, PPC LiDAR enables parallel ranging up to 165 m in a single‐channel, beyond the distance threshold limited by the pulse period. In the outdoor imaging experiment, parallel imaging over 40 m was achieved. By combining 10‐channel parallel detection with robust noise resistance, PPC LiDAR provides a light‐source‐based solution for single‐photon LiDAR operation under extreme conditions, distinct from experimental quantum illumination. Our PPC LiDAR scheme opens a new pathway toward the next generation of high‐performance environmental perception technology.

Authors

Institutions

Publication Details

Journal
Laser & Photonics Review
Published
2026-10-26
DOI
https://doi.org/10.1002/lpor.72144
Primary Topic
Advanced Optical Sensing Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Parallel Photon‐Correlation LiDAR With Robust Noise Resistance Driven by Super‐Bunching Light

Zhichun Yang, Aoni Wei, Yunrui Song, Chengbing Qin et al.
Laser & Photonics Review
Advanced Optical Sensing Technologies
article

Parallel Photon‐Correlation LiDAR With Robust Noise Resistance Driven by Super‐Bunching Light

Zhichun Yang, Aoni Wei, Yunrui Song, Chengbing Qin, Xue-Dong Zhang, Guofeng Zhang, Xinghui Liu, Jianyong Hu, Xiangdong Li, Yuanyuan Li, Xilong Liang, Liantuan Xiao, Suotang Jia, Ruiyun Chen
article en

Abstract

ABSTRACT High‐performance single‐photon light detection and ranging (LiDAR) technology with robust noise resistance provides a powerful means for environmental perception in extreme scenarios. Here, we propose a parallel photon‐correlation (PPC) LiDAR driven by super‐bunching light, enabling noise‐resistant parallel detection in the single‐photon‐counting regime. Using super‐bunching light with g (2) (0) = 177, we demonstrate robust photon‐correlation imaging at a signal‐to‐background ratio (SBR) of 1:2750 in the 2D parallel imaging experiment, and also achieve noise‐resistant three‐dimensional (3D) target reconstruction. Leveraging the correlation between the reference and echo signals, PPC LiDAR enables parallel ranging up to 165 m in a single‐channel, beyond the distance threshold limited by the pulse period. In the outdoor imaging experiment, parallel imaging over 40 m was achieved. By combining 10‐channel parallel detection with robust noise resistance, PPC LiDAR provides a light‐source‐based solution for single‐photon LiDAR operation under extreme conditions, distinct from experimental quantum illumination. Our PPC LiDAR scheme opens a new pathway toward the next generation of high‐performance environmental perception technology.

Laser & Photonics Review
Shanxi Medical University (CN), Shanxi University (CN), Taiyuan Central Hospital (CN), State Key Laboratory of Quantum Optics and Quantum Optics Devices (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 14%
Advanced Optical Sensing Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon