Parallel computation of regional CORS network corrections based on ionospheric-free PPP

Abstract

Global navigation satellite system real-time processing requires low latency, high timeliness, and high computational efficiency. A typical application is providing corrections using data from a regional Continuously Operating Reference Station (CORS) network. Usually the wide-lane and narrow-lane fractional cycle biases (FCBs) are determined at the server and broadcast to users to fix undifferenced ambiguity. Also, a tropospheric model is established at the server and broadcast to users to obtain accurate and reliable a priori zenith total delays for precise point positioning (PPP) using the ionospheric-free (IF) observation combination. Currently, serial methods are typically applied, i.e., all reference stations are involved in estimating the wide-lane and narrow-lane FCBs and establishing a regional tropospheric delay model. To improve the efficiency and shorten the latency, we develop a parallel computation method for regional CORS network corrections based on IF PPP by adopting a multicore parallel computing technology task parallel library, wherein parallel computations involving the FCBs, tropospheric delays, and tropospheric model are successively performed based on data parallelism, in which the same operation is performed concurrently on elements in an array, and task parallelism, which refers to one or more independent tasks running concurrently. Data covering four seasons from the Hong Kong and southwestern America CORS networks are utilized in the experiment. The single differenced FCBs between satellites are determined within each full pass, and a tropospheric model with an internal accuracy better than 1.4 cm and an external accuracy better than 1.6 cm is derived at the server. With the parallel implementation, the speedup ratios of FCB estimation and tropospheric modeling are 1.79, 3.15, 5.59, and 9.69 times higher for dual-core, quad-core, octa-core, and hexadeca-core platforms, respectively, than for a single-core platform.