Improving Precise Point Positioning Convergence Time through TEQC Multipath Linear Combination

Abstract

Pseudorange multipath and noise are still considered the major sources of error in precise point positioning (PPP) solutions. If multipath and noise were correctly mitigated from the pseudoranges, the accurate estimation of float ambiguities could be achieved rapidly; thus, a reduction in PPP convergence time would be realized. In this study, three different methods were used: (1) the known multipath linear combination using the postprocessed (PP) average; (2) the multipath linear combination with the moving average by translation, editing, and quality check (TEQC) software; and (3) the modified stochastic modeling or weights based on the multipath linear combination. TEQC software is one of the most famous tools used to solve the preprocessing problems for global navigation satellite system (GNSS) data. TEQC software was used to provide multipath corrections according to a multipath linear combination that depends on the moving average algorithm for a seven-day data set from 75 international global positioning system service stations. The improvements were computed by comparing the proposed methods with the traditional PPP solution for which all pseudorange measurements are equally weighted. The results of this study indicate that the improvements of the convergence time were achieved for 32 and 41.5% of the data set using the postprocessed multipath linear combination and the modified weighting scheme derived from a TEQC multipath linear combination, respectively. Also, the results indicate that only the traditional PPP enhanced by the TEQC multipath linear combination and the traditional PPP have the same convergence performances.