Parallel Computation of Multi-GNSS and Multi-Frequency Inter-Frequency Clock Biases and Observable-Specific Biases

Abstract

With the widespread application of GNSS, the delicate handling of biases among different systems and different frequencies is of critical importance, wherein the inter-frequency clock biases (IFCBs) and observable-specific signal biases (OSBs) should be carefully corrected. Usually, a serial approach is used to calculate these products. To accelerate the computation speed and reduce the time delay, a multicore parallel estimation strategy for IFCBs, code, and phase OSBs by utilizing task parallel library (TPL) is proposed, the parallel computations, including precise point positioning (PPP), IFCBs, and OSBs estimation, being carried out on the basis of data parallelisms and task-based asynchronous programming. Three weeks of observables from the multi-GNSS experiment campaign (MGEX) network is utilized. The result shows that the IFCB errors of GPS Block IIF and GLONASS M+ satellites are nonnegligible, in which the GLONASS M+ satellite R21 shows the largest IFCB of more than 0.60 m, while those of other systems and frequencies are marginal, and the code OSBs present excellent stability with a standard deviation (STD) of 0.10 ns for GPS and approximately 0.20 ns for other satellite systems. Besides, the phase OSBs of all systems show the stability of better than 0.10 ns, wherein the Galileo satellites show the best performance of 0.01 ns. Compared with the single-core serial computing method, the acceleration rates for IFCBs and OSBs estimation are 3.10, 5.53, 9.66, and 17.04 times higher using four, eight, sixteen, and thirty-two physical cores, respectively, through multi-core parallelized execution.