## 1 Introduction  
Urban canyon is one of the most challenging environments for global navigation satellite system (GNSS) positioning. The high buildings and skyscrapers can easily block or reflect the GNSS signal to induce the signal delay, which are well-known as the multipath effect and non-line-of-sight (NLOS) receptions. These signal reflection effects limit the application of the GPS positioning in city urban area, for example, the pedestrian localization for the applications of the pedestrian's safety in the intelligent transportation system (ITS). The technology of autonomous driving also requires accurate and reliable positioning services in urban areas. Many studies are therefore focused on improving GNSS positioning performance in the degraded environments. Conventionally, the multipath can be mitigated using sophisticated antenna designs [1,2] and receiver-based discriminator designs [3,4]. The multipath mitigation methods mentioned above have little improvement on NLOS reception. Approaches to NLOS mitigation are therefore needed. With the aid of receiver dynamic provided by inertial sensor, tightly and ultra-tightly coupled GPS/INS integrations are proposed to mitigate the multipath and NLOS effects [5, 6, 7, 8, 9]. A multipath estimation, which is based on integration between GNSS receiver and LiDAR sensor, is proposed [10]. Novel receiver-based techniques are also proposed to detect multipath and NLOS signals [11,12]. One of the newly proposed solutions is to take advantage of the city building models [13, 14, 15, 16, 17, 18, 19, 20, 21, 22] to mitigate, detect or even correct the reflection signals. In 2013, The research team of The University of Tokyo developed a particle filter based positioning method using a basic three-dimension (3D) city building to estimate the positioning result of commercial GNSS single frequency receiver [23, 24, 25]. Figure 1 shows the basic idea of the proposed 3D building model positioning method. The proposed method first distributes the random position candidates, and then calculates the pseudorange similarities between raw pseudorange measurements and the simulated pseudoranges (estimated by the help of the 3D building models and ray-tracing technique [26]) for each candidate. The likelihood of each candidate is based on its pseudorange similarity. Finally, the weighted average of the positions of the candidates is regarded as the estimated position result. The studies that corrected NLOS and used it as an additional measurement, including the proposed building model based positioning method, assumes the travelling path of the NLOS is single reflected [18,20,25]. In the case of general urban canyons, this assumption should be correct in most cases. Because  
Figure 1: The idea of the developed 3D building model based positioning method. buildings in middle or deep urban canyons, the possible reflection paths of GNSS signals are increased dramatically. As a result, a double or multiple reflected NLOS signal can be easily observed. These multiple reflection signals increase the difficulty and immense computational load of using the signal ray racing to track the signal reflecting routes. As a result, abnormal signal exclusion is required in the positioning method. A practical NLOS signal exclusion algorithm, called consistency check, has been proposed recently [27]. This consistency check follows the idea of receiver autonomous integrity monitoring (RAIM) fault detection and exclusion (FDE) to exclude abnormal signals by the pseudorange residual [28, 29]. This paper is inspired by the consistency check used in the weighted least square (WLS) method. Instead of applying the RAIM in the conventional positioning method, this paper applies the RAIM in the developed 3D building model based positioning method to exclude the abnormal reflection signals. Accordingly, this paper is organized as follows: The related works on GNSS 3D map method and RAIM FDE are given in Section 2. A brief introduction of the 3D building model positioning method is introduced in Section 3.