Open AccessMultipath Suppressing Method Based on Pseudorange Model Using Modified Teaching‐Learning Based Optimization Algorithm
Author(s) -
CHENG Lan,
ZHANG Jing,
NI Zihang,
YAN Gaowei
Publication year - 2022
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.00.168
Subject(s) - pseudorange , computer science , multipath propagation , particle swarm optimization , gnss applications , differential evolution , algorithm , precise point positioning , satellite system , global positioning system , stability (learning theory) , machine learning , telecommunications , channel (broadcasting)
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 based positioning algorithm, differential evolution based positioning algorithm, variable projection method, and TLBO algorithm, in terms of accuracy and stability.