A fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation. Issue 387 (2nd November 2022)

• Record Type: Journal Article
• Authors: Guan, Xiaoguo; Chai, Hongzhou; Xiao, Guorui; Han, Jie; Han, Shijing; Shufeng, Miao
• Is Part Of: Survey review. Volume 54:Issue 387(2022)
• Page Start: 554
• Page End: 565
• Publication Date: 2022-11-02
• Subjects: Improved particle swarm optimisation -- Fast satellite selection -- Multi-GNSS marine positioning -- GDOP -- linear inertia weight -- Immune system -- Precision point positioning; Surveying -- Periodicals; Great Britain -- Surveys -- Periodicals; 526.9
• DOI: 10.1080/00396265.2021.1991175
• Languages: English
• ISSNs: 0039-6265

Abstract

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.