In recent years interest has increased for precise point positioning (PPP), which provides an alternative to precise relative processing because of its potential as a reliable absolute positioning technique. PPP technique takes advantage of satellite orbit and clock products obtained from the global infrastructure of permanent stations. As a result, PPP requires only one dual-frequency, carrier-phase global positioning system (GPS) receiver on the client side and, thus, avoids the expensive and logistic difficulties of deploying a network of global navigation satellite system (GNSS) receivers around survey areas or in isolated places, such as the Arctic or less populated areas. The broad question of the place of PPP in the future spectrum of space geodetic measurement techniques is addressed in this study by investigating specific aspects of PPP. These aspects involve the following: (1) the time convergence solution in coordinates and accuracy; (2) the observational time required to guarantee the absolute...

Precise point positioning (PPP) involves observations from a single global navigation satellite system (GNSS) receiver and benefits of satellite orbit and clock products obtained from the global infrastructure of permanent stations. PPP avoids the expense and logistic difficulties of deploying a network of GNSS receivers around survey areas in isolated places, such as the arctic or less populated areas. Potential accuracies are at the centimeter level for static applications and at the subdecimeter level for kinematic applications. Static and kinematic PPP based on the processing of global positioning system (GPS) observations is limited by the number of visible satellites, which is often insufficient for urban or mountain applications, or it can be partially obstructed or present multipath effects. Even if a number of GPS satellites are available, the accuracy and reliability can still be affected by poor satellite geometry. One possible way of increasing satellite signal availability and positio...

Real-time precise point positioning (PPP) has become a prevalent technique in global navigation satellite systems (GNSS). However, GNSS real-time users must receive space state representation (SSR) products to correct for satellite clock, orbit, and phase biases. The International GNSS Service (IGS) provides GNSS users with real-time services (RTSs) through different real-time correction SSR products. These products arrive at the GNSS users with some latency, which affects the quality of real-time PPP positioning. The autoregressive integrated moving average (ARIMA) and support vector regression (SVR) models are used in this research to predict those corrections to eliminate the latency effect. ARIMA model reduces the standard deviation by 28% and 13% for GPS and GLONASS constellations, respectively, compared to the real-time solution, which includes the latency effect, the research simulated the latency effect and named it a forced-latency solution, and the SVR model reduces the standard deviation by 28% and 23% for GPS and GLONASS constellations, respectively. The results for the permanent GNSS stations used in this study across different years 2013, 2014, 2015, 2019, and 2021 show a mean reduction in the 3D positioning standard deviation by 13% compared with the forced-latency solution for the ARIMA solution and 9% for the SVR solution. The potential of both models to overcome the latency effect is apparent based on the findings.

In recent years, Global Navigation Satellite Systems (GNSSs) have become integral to our daily lives because of their precise positioning and navigation capabilities. Widespread use of smartphones equipped with GNSS receivers results in the generation of a huge amount of positioning data. Therefore, real-time cloud computing has emerged as a promising approach to effectively leverage this wealth of location information. In this study, we developed an Android app that captures raw GNSS data from smartphones, leverages cloud computing resources, calculates the position of the device, and returns the computed solution to the user. Integration of cloud-based processing not only conserves the device resources but also enables real-time position calculation, paving the way for enhanced location-based applications and services.