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1.Research Institute of Superconductor Electronics & Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University, Nanjing 210023, China
2.College of Engineering and Applied Sciences, Nanjing University, Nanjing 210023, China
3.Hefei National Laboratory, Hefei 230088, China
4.Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense, Nanjing University of Science and Technology, Nanjing 210094, China
Labao Zhang (Lzhang@nju.edu.cn)
Lijian Zhang (Lijian.zhang@nju.edu.cn)
Weiji He (hewj@mail.njust.edu.cn)
Peiheng Wu (phwu@nju.edu.cn)
Received:18 September 2024,
Revised:14 February 2025,
Accepted:16 February 2025,
Published Online:26 March 2025,
Published:31 May 2025
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Li, H. C. et al. Noise-tolerant LiDAR approaching the standard quantum-limited precision. Light: Science & Applications, 14, 1387-1397 (2025).
Li, H. C. et al. Noise-tolerant LiDAR approaching the standard quantum-limited precision. Light: Science & Applications, 14, 1387-1397 (2025). DOI: 10.1038/s41377-025-01790-5.
Quantum-inspired imaging techniques have been proven to be effective for LiDAR with the advances of single photon detectors and computational algorithms. However
due to the disturbance of background noise and the varies of signal in outdoor environment
the performance of LiDAR is still far from its ultimate limit set by the quantum fluctuations of coherent probe light. In this work
we propose and demonstrate a LiDAR from the detection perspective for approaching the standard quantum-limited performance. The photon numbers of echo signals are recorded by a photon-number-resolving detector and applied to overcome heavy background noise through an active photon number filter in the LiDAR. It can approach the standard quantum limit in intensity estimation in a wide photon-flux range
and achieve a Fisher information of only 0.04 dB less than the quantum Fisher information when the mean signal photon number is 10. Experimentally
a noise-free target reconstruction and imaging is demonstrated in the daytime by the proposed LiDAR. It also performs better in reflectivity resolution when taking only 1/1000 of the measurements based on on/off detection. This work provides a fundamental strategy for constructing a LiDAR to quickly extract targets and identify materials in complex environments
which is important for intelligent agents such as autonomous vehicles.
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