## 1 Introduction  
A robot's localization is the problem of estimating the robot's position relative to its environment from sensor observations. Localization is a necessity for successful mobile robot systems; it is the most fundamental problem in providing a mobile robot with autonomous capabilities [1]. The robot must maintain an accurate knowledge of its position and orientation to achieve autonomous navigation [2]. Many techniques are used for robot localization such as visual-based localization (the use of cameras on the robot to detect and recognize landmarks in the environment [3]; these landmarks are then used to estimate the robot's position), simultaneous localization and mapping (SLAM) (enables a robot to create a map of an unknown environment while simultaneously determining its position within the map) [4,5], radio frequency identification (where tags can be placed in the environment, and the robot can detect these tags and use them to determine its position), inertial-based localization (inertial sensors like accelerometers and gyroscopes can be used to track the robot's motion and estimate its position and orientation), long-term evolution (LTE) and wheel odometry-based localization (in the case of GPS-denied outdoor environments, LTE filter and the mobile robot position is estimated given the applied changes in odometry and received LTE [6]. However, the root-mean-square error (RMSE) of the position accuracy results is 13.07 m, and GPS-based localization (GPS can be used for indoor [7] or outdoor robot localization [8; 9; 10; 11; 12; 13] by providing the robot's position relative to the global coordinate system). GPS-based localization is considered more cost effective compared with other techniques [14; 15; 12]. GPS is a satellite-based global positioning system that started with the USA system (GPS) (1991) and evolved into four global systems: USA (GPS), Russia (GLONASS), China (BeiDou), and Europe (Galileo). Those GNSSs offer a greater number of visible satellites with better distribution and more civilian signals that ensure better accuracy for robot localization. GPS-based localization is ideal for outdoor environments and not suitable for indoor environments because of the lack of satellite signals. However, integration of GPS/INS systems could be used to account for satellite outage intervals [16; 17; 14; 18]. GNSSs are satellite-based systems for measuring position and time on Earth. The accuracy of the estimated position depends mainly on the number of employed satellites. The issue of an accurate, reliable, and secure GNSS-based localization and navigation approach for mobile robotics has been widely addressed by several research and industrial projects, and numerous solutions have been developed. GPS and GLONASS are the two GNSSs that are currently accessible and commonly used in mobile robotics for robot localization. The Galileo and BeiDou systems are emerging GNSSs for mobile robot applications. Additional sensors and/or filter-based systems can be combined with GNSS to enhance position accuracy [19; 20], such as Galileo E5 AltBOC with inertial measurement sensors [21], the real-time kinematics differential global position system (RTK-DGPS) with a laser range finder [22], and the low-cost RTK-GPS [23; 24] (position accuracy of 0.08 m-0.1 m). [PERSON] et al. confirmed the performance of GNSS and satellite/ground-based augmentation systems for the autonomous lawn mower's localization and navigation [25]. The assessment was performed based on a comparison of the proposed localization algorithm with the traditional differential GPS positioning system. The satellite/ground-based augmentation systems-based localization solution presents technical barriers for a lot of potential customers due to the lack of global coverage of augmentation systems. [PERSON] et al. developed a new path-planning algorithm for unmanned aerial vehicles reducing the potential GPS positioning error due to obstacles [26]. The mean real compiled positioning error is reduced from 17.52 m to 4.94 m. [PERSON] et al.