A fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation

Abstract

This paper introduces an improved particle swarm optimisation algorithm (IPSO), to select satellites rapidly in multi-GNSS marine positioning. The traditional particle swarm optimisation (PSO) may be trapped into local optimisation. To avoid the disadvantage, the proposed algorithm uses linear inertia weight factor and two functions of the immune system, i.e. the memory function and the self-regulatory function. Several experiments are carried out by adopting real survey data collected by the SiNan receiver that is installed on the Snow Dragon scientific research ship during the 9th China Arctic expedition. Compared with the minimum Geometric dilution of precision (GDOP) method, PSO and IPSO significantly reduce the computing time (96.25% and 95.61%). The variance of IPSO is 0.063, which is much lower than that of PSO (0.087). As for the positioning accuracy, the IPSO can reach the centimetre level in the kinematics condition.

Keywords:

• Improved particle swarm optimisation
• Fast satellite selection
• Multi-GNSS marine positioning
• GDOP
• linear inertia weight
• Immune system
• Precision point positioning

Acknowledgements

Thanks to China's 9th Arctic Science Expedition for its support of data collection in this paper. G.X. designed and performed the experiments, analysed the data, and wrote the manuscript. C.H. helped perform the experiments. X.G. made a critical revision of the manuscript. H.J. developed the concept. H.S. helped analyse the data. M.S. helped write the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

National Natural Science Foundation of China (grant number 41904039); Science and Technology Department of Henan Province under contract Nos 212102310029 and 202102310015; Scientific research project of Xuchang University under contract No. 2021ZD009; Educational teaching research and practice project of Xuchang University under contract No. XCU2021-YB-024.

Notes on contributors

Xiaoguo Guan

Xiaoguo Guan, received the D.R degree in geodesy and surveying engineering from Information Engineering University, Zhengzhou, China. She is currently a teacher at Xuchang University, Xuchang, China. Major: Geodesy and Measurement Engineering, main research direction: BDS/GNSS precision positioning data processing. E-mail: [email protected], Address: No. 88, Avenue, College Road Xuchang City, Henan Province, Zip code: 461000.

Hongzhou Chai

Hongzhou Chai, is a professor in Information Engineering University, Zhengzhou, China. Major: Geodesy and Measurement Engineering, main research direction: Marine BDS/GNSS precision positioning data processing.

Guorui Xiao

Guorui Xiao, is a teacher in Information Engineering University, Zhengzhou, China. Major: Geodesy and Measurement Engineering, main research direction: GNSS precision positioning data processing.

Jie Han

Jie Han, is a teacher in Xuchang University, Xuchang, China. Major: Geodesy and Measurement Engineering, main research direction: Remote Sensing data processing.

Shijing Han

Shijing Han, is studying for her doctorate in Information Engineering University, Zhengzhou, China. Major: Geodesy and Measurement Engineering, main research direction: Image data processing.

Miao Shufeng

Miao Shufeng, is a branch manager and senior engineer in Wuhan Kedao Geographical Information Engineering Co., Ltd, Wuhan, China. Major: Geodesy and Measurement Engineering, main research direction: project management, digital image processing, navigation and location services.