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CHENG Lan, ZHANG Jing, NI Zihang, YAN Gaowei. Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.168
 Citation: CHENG Lan, ZHANG Jing, NI Zihang, YAN Gaowei. Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm[J]. Chinese Journal of Electronics.

Multipath Suppressing Method Based on Pseudorange Model Using Modified Teaching-Learning Based Optimization Algorithm

doi: 10.1049/cje.2020.00.168
Funds:  This work was supported by the National Science Foundation of China (No. 62073232, No. 61973226)
• Author Bio:

was born in Guangshan, Henan, she received the M.S. degree in control theory and control engineering from Taiyuan University of Technology in 2008 and the PhD. degree in control science and engineering from Beijing Institute of Technology in 2012. She is currently an assistant professor with the College of Electrical and Power Engineering, Taiyuan University of Technology. Her research interests include multipath mitigation for GNSS and simultaneous localization and mapping for robots

received the M.S. degree in control theory and control engineering from Taiyuan University of Technology in 2019. She is currently with Xi’an Branch of China Telecom Co. LTD. Her research interest is multipath mitigation for GNSS

is currently a graduate student in Taiyuan University of Technology. His research interest is differential evolution algorithm and its application in multipath mitigation

received the M. S. degree in control theory and control engineering, in 2003 and the PhD degree in circuits and systems, in 2007, both from Taiyuan University of Technology. He is currently a professor with the College of Electrical and Power Engineering, Taiyuan University of Technology. His research interests include intelligent control theory and application, evolutionary computation, knowledge discovery and intelligent information processing

• Accepted Date: 2021-10-21
• Available Online: 2022-01-06
• Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments (such as dense urban, valley), multipath interference is one of the main error sources deteriorating positioning accuracy, and it is difficult to eliminate via differential techniques due to its uncertainty of occurrence and irrelevance in different instants. To address this problem, we propose a positioning method for Global navigation satellite systems (GNSS) by adopting a modified Teaching-Learning based optimization (TLBO) algorithm after the positioning problem is formulated as an optimization problem. Experiments are conducted by using actual satellite data. The results show that the proposed positioning algorithm outperforms other algorithms, such as Particle swarm optimization (PSO) based positioning algorithm, Differential evolution (DE) based positioning algorithm, Variable projection (VP) method, and TLBO algorithm, in terms of accuracy and stability.
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