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With the accelerated development of multi constellation Global Navigation Satellite Systems (GNSS), the field of satellite navigation is going through profound changes. Depending on a single constellation may not be sufficient to guarantee a successful positioning accuracy at any time. Consequently, having several constellations that can be used for positioning offers the opportunity to maintain continuous positioning. This became possible with the global coverage of other constellations such as (BeiDou, GLONASS, Galileo, and GPS). For engineering and scientific applications, this will present both opportunities and obstacles. This paper develops a 4-system positioning model for all the available observations from other GNSS and benefits from them. The important enhancement of satellite visibility, precision dilution, spatial geometry, accuracy, convergence, continuity, and reliability that combined use of multi-GNSS achieve to accurate positioning is attentively inspected and assessed, particularly in strained environments, overall, in the horizontal and 10 cm vertical components with precise orbits the multi-GSS PPP has a precise accuracy greater than 4 cm.

he demand for navigation systems is rapidly increasing, especially in GNSS-denied environments. The ubiquitous use of smart mobile devices equipped with various sensors encouraged many researchers to investigate their use in improving indoor navigation, where GNSS is not available. Inertia navigation sensors installed in mobile devices are normally low cost and drift significantly. Consequently, there is a need for auxiliary systems to aid the navigation process, which can be achieved using external sensors or additional information extracted from, for example, base maps. In this research paper, maps have been selected as a navigation aid. Previously, maps were used for navigation aiding through geospatial data models and map-matching algorithms. These methods are based on creating geospatial data models on the fly and integrating them in the navigation database, which makes them computationally expensive and time-consuming. In this research paper, the maps were used in an innovative way. The map directions were used in Pedestrian a dead reckoning (PDR) mode to improve the low-accuracy directions derived from portable device sensors. This method is significantly computationally efficient compared to traditional geospatial map-matching algorithms. The new approach replaces the traditional geospatial database with a list of street directions and paths that are used as Map Heading Constraints (MHC) when navigating (walking) in straight directions. The proposed technique was tested on trajectories in GNSS denied environment (underground parking) using an iphone6s smart-phone and compared with other solutions that used the portable device sensors only. The comparison showed a significant improvement in position accuracy (up to 90%) in comparison to using the portable device sensors only (no aiding).