A Partial Ambiguity Resolution Algorithm Based on New-Breadth-First Lattice Search in High-Dimension Situations

Shouhua Wang; Zhiqi You; Xiyan Sun; Libo Yuan

doi:10.3390/s22197126

A Partial Ambiguity Resolution Algorithm Based on New-Breadth-First Lattice Search in High-Dimension Situations

Sensors (Basel). 2022 Sep 20;22(19):7126. doi: 10.3390/s22197126.

Authors

Shouhua Wang^{1

2

3}, Zhiqi You^{1

3}, Xiyan Sun^{1

3}, Libo Yuan⁴

Affiliations

¹ School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
² Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
³ National Engineering Research Center for Satellite Navigation, Positioning and Position Service, Guilin 541004, China.
⁴ School of Optoeletronic Engineering, Guilin University of Electronic Technology, Guilin 541004, China.

Abstract

With the development and integration of GNSS systems in the world, the positioning accuracy and reliability of GNSS navigation services are increasing in various fields. Because the current multisystem fusion leads to an increase in the ambiguity dimension and the ambiguity parameters have discrete characteristics, the current conventional search algorithm leads to low search efficiency when the ambiguity dimension is large. Therefore, this paper describes a new algorithm that searches the optimal lattice points by lattice theory through the breadth-first algorithm and reduces the search space of ambiguity by calculating and judging the Euclidean distance between each search variable and the target one so as to propose a new lattice ambiguity search algorithm based on the breadth-first algorithm. The experimental results show that this method can effectively improve the search efficiency of ambiguity in high-dimension situations.

Keywords: RTK sensor networks; high-dimension situations; lattice theory; partial ambiguity resolution.

Grants and funding

This work was supported by the National Natural Science Foundation of China (62161007), the Guangxi Natural Science Foundation (2018GXNSFAA050123), and The Graduate Education Innovation Project of Guangxi (YCSW2021180).