An integer ambiguity clustering estimator for real-time precise point positioning - Kunlun Zhang; Jianghui Geng; Guangcai Li; Jiang Guo (Geo-spatial Information Science)
Real-time precise point positioning with ambiguity resolution (PPP-AR) is considered a valuable positioning tool applicable in various fields. Limited by the precision and stability of real-time satellite products and observation streams, it is challenging to continuously and correctly resolve the ambiguity, resulting in positioning fragmentation in real-time PPP-AR. Therefore, inspired by the best integer equivariant (BIE) method, we proposed an integer ambiguity clustering estimator (IACE) for improving real-time PPP-AR performance. IACE performs hierarchical agglomerative clustering (HAC) based on ambiguity correlations, enabling a direct approximation of the final candidate through intra- and inter-cluster enumeration. It further introduces the constraints of the fixed ambiguities into the filter with cascading weights, aimed at resisting the influence of potentially incorrect ambiguity resolution. We selected 115 multi-GNSS stations worldwide and carried out 22 days of continuous real-time PPP-AR. It is found that IACE improves the computational efficiency across various ambiguity dimensions compared to classic partial ambiguity resolution (PAR) and BIE, while improving the positioning precision over the float solution by 26.1%, 10.4%, and 5.6% in the east, north, and up components, respectively, overall comparable to the PAR and BIE. Regarding the entire position time series, the IACE-induced jumping rate is 0.04%, while PAR and BIE are 0.37% and 0.28%, respectively. This indicates that IACE effectively resists positioning jumps in the ambiguity-fixed solutions. Moreover, IACE exhibits lower displacement wanders across the time scale of 1 to 104 s compared to the other strategies, showing overall superior positioning capability.