# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 1 Introduction  
The growing number of applications of autonomous vehicles and the fact that driving autonomous vehicles in any level of automation is a safety critical application necessitate very precise and accurate determination of the positions of such vehicles, with a high level of integrity. The levels of autonomy range from zero level (no automation) to level five (fully automated without human interaction or even attention) [1]. Finding the precise position of the vehicle (i.e., vehicle localization) is a fundamental part of the next steps of autonomous vehicle driving which further includes perception, prediction, planning and control [2]. The only sensor that provides an absolute position of objects is the Global Navigation Satellite System (GNSS) [3, 4]. The signals transmitted from these satellites in L-band are strongly attenuated due to the long distance (around 20,000 km) of the satellite from the Earth's surface [5]. In addition, atmospheric refraction, satellite and receiver clock errors and orbit errors impact the observation error budget, to just name some of the issues [6]. The error contributions which are common or very similar for two receivers at different locations or between two satellites observed by one receiver can be eliminated or at least largely reduced by differencing, e.g., double differencing (DD) [7]. Examples of such errors are delays by ionospheric and tropospheric refraction, satellite orbit errors, and satellite and receiver clock errors. Real-time kinematic (RTK) positioning solutions rely on very precise carrier phase observations (mm noise level) which are two orders of magnitude (i.e., factor 100) more precise than pseudorange observations. In Network RTK (NRTK), a network of several reference stations is producing and distributing corrections for thedistance-dependent error sources [8]. NRTK solutions deliver cm level accurate position results in optimum situations. If the number of observations is not high enough or if their quality is not good enough, the cm precision is not guaranteed and the solution degrades to the dm or even m level. The availability of sufficient dual-frequency phase observations with high C/N\({}_{0}\) is a key factor for high-precision RTK positioning. However, location-specific errors remain, which are not common between receivers nor between satellites, namely multipath or non-line-of-sight (NLOS) signal reception [9]. These kinds of errors are more severe in urban environments where GNSS signals are interacting with different obstacles such as buildings, bridges, trees, utility poles, etc. [10; 11]. For mitigating NLOS and multipath effects, different approaches have been used, namely antenna and receiver design, signal processing techniques, navigation-based techniques, postprocessing and 3 DMA (three-dimensional-mapping-aided) techniques [12], which include ranging based and shadow matching, i.e., comparing the C/N\({}_{0}\) to nominal values to determine blockages. Here, our focus is on the 3 DMA approach. The work conducted at University College London (UCL) in collaboration with Spinert company [13; 14] shows the satellite signal can be received rather than LOS, in diffracted, reflected and multipath situations. \"SPRING\", a simulating software developed at a French space agency (CNES) in collaboration with Thales Services company, is a toolbox which considers the reflection and diffraction of the signal, and [15] shows the NLOS effect on positioning error. The physical and statistical properties of the multipath are considered in the \"SCHUN\" simulator developed at French national aerospace research laboratory (ONERA) [16]. Researchers at Gustave Eiffel University performs ray-tracing algorithms to forecast the satellite visibility [17] and to correct the NLOS observations for better positioning results [18; 19; 20]. 3 DMA in the work of IPN Laboratory of Hong Kong Polytechnic University [21] is used to weigh the NLOS observations rather than exclude them.