A Few-Shot Learning-Based Semi-Supervised Method to Enhance NLOS Recognition Accuracy Across Multiple Locations by Cross-Spatiotemporal Information-Reference-Cited by-同舟云学术

A Few-Shot Learning-Based Semi-Supervised Method to Enhance NLOS Recognition Accuracy Across Multiple Locations by Cross-Spatiotemporal Information

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zeng Kungan¹^ORCID,Li Zhenni¹^ORCID,Xie Shengli²^ORCID,Wang Qianming¹^ORCID,Chen Wuhui³^ORCID,Zheng Zibin³^ORCID

Affiliation:

1. School of Automation, Guangdong University of Technology, Guangzhou, China

2. Guangdong Key Laboratory of IoT Information Technology and the Key Laboratory of Intelligent Information Processing and System Integration of IoT, Guangdong University of Technology, Ministry of Education, Guangzhou, China

3. School of Software and Engineering, Sun Yat-Sen University, Zhuhai, China

Funder

National Natural Science Foundation of China

Guangdong Basic and Applied Basic Research Foundation

National Key Research and Development Plan-Strategic Scientific and Technological Innovation Cooperation Key Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10630876.pdf?arnumber=10630876

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5. Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons