# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 1 Introduction  
The demand for the Global Navigation Satellite System (GNSS) has increased in recent years because many applications use the GNSS receiver as a positioning device. With the advent of the low-cost multi-GNSS and multi-frequency receivers, the use of high-accuracy positioning will be accelerated [1]. The real-time kinematic GNSS (RTK-GNSS) is a popular technique for achieving precise positions at the centimeter level because mobile network operators have been installing GNSS base stations in Japan since 2018 [2]. Furthermore, the Centimeter Level Augmentation Service has officially been implemented through the Quasi-Zenith Satellite System (QZSS) since November 2018 [3]. The RTK-GNSS requires observation data from the base station, which means that it is easy to use provided a GNSS device and mobile network are available. Based on our various test results using a car in Tokyo, the performance of the RTK-GNSS using a low-cost GNSS receiver was found to be relatively good, and the fix rate was always over 80% to 90% in normal urban areas near our university campus. However, it is well known that the fix rate decreases dramatically when moving into dense urban areas surrounded by many high-rise buildings. In reality, the RTK-GNSS is almost impossible to implement on a street surrounded by many high-rise buildings. In addition, RTK-GNSS has been used in surveys for construction in open sky conditions. However, the requirement of GNSS surveys in challenging areas is increasing. For example, landslide monitoring under conditions in which the sky is approximately half blocked and surveying nearby high-rise buildings. Automatic parking position management using GNSS is also becoming popular. If the parking location is in dense urbanareas, there are numerous NLOS signals. To achieve RTK-GNSS positions and better (Differential GNSS) DGNSS positions, NLOS detection is very important even in the case of static conditions. Therefore, we focused on improving the RTK-GNSS and DGNSS in such a challenging area. The key to improving the performance of the RTK-GNSS is whether satellites contaminated by large multipath errors can be detected, and approximately 1- to 2-m level accuracy is required for reliable carrier phase ambiguity resolution of the RTK-GNSS [4]. Therefore, the use of 3D building models and the fish-eye view have been proposed [5; 6; 7]. Multipath detection using machine learning has also been presented in recent years [8]. Furthermore, GNSS/Inertial Navigation System (INS) integration has been proposed for continuous positioning and was found to be effective in reducing the large jumps resulting from multipath errors [9]. Although the abovementioned methods are powerful tools for detecting non-line-of-sight (NLOS) signals, the performance of the RTK-GNSS needs to be improved. The aim of this study was to demonstrate how to detect the satellite of an NLOS signal using only the GNSS. We have previously presented several methods using the carrier to noise ratio (C/N\({}_{0}\)) to reduce multipath errors, one of which is to set the elevation-dependent threshold of C/N\({}_{0}\)[10]. If C/N\({}_{0}\) is over this threshold according to the elevation angle, the satellite is used for positioning. This method is relatively effective for detecting NLOS signals. However, the C/N\({}_{0}\) of the NLOS signal occasionally reaches the same signal level as the usual line-of-sight (LOS) signal. Another method is to use the C/N\({}_{0}\) fluctuation magnitude for a certain period [11]. The disadvantage of this approach is that it is difficult to set the optimal threshold. In fact, the magnitude of the multipath signal fluctuation in dense urban areas frequently changes from small to large. We generally use the weighted least-squares method to determine the position, which provides the position as well as each residual for the satellites used in positioning. These residuals can be used for consistency checks to detect satellites with large residuals [12].