A Study of City Building Model Based Positioning Method Using Multi-gnss in Deep Urban Canyon
نویسندگان
چکیده
The current global navigation satellite systems (GNSS) receiver cannot calculate satisfactory positioning results in urban environment due to multipath and non-line-of-sight (NLOS) effects. The research team of The University of Tokyo developed a particle filter based positioning method using a basic 3-dimension city building to rectify the positioning result of commercial GPS single frequency receiver. This developed method is achieved by implementing particle filter to distribute possible position candidates. The likelihood of each candidate is evaluated based on the similarity between the pseudorange measurement and simulated pseudorange of the candidate. The expectation of all the candidates is the rectified positioning of the proposed map method. The evaluation of using QZSS L1-submeter-class augmentation with integrity function (L1-SAIF) correction to the developed pedestrian positioning method is also previously discussed. However, the visible satellite in deep urban canyon using only GPS and QZSS would be not very enough for the developed method. The use of the immerging multi-GNSS, such as GLONASS, Galileo and Beidou, could be a potential solution to the situation of lacking of satellites for the developed method. The real data are recorded in two of the most famous urban canyon, Shinjuku and Hitotsubashi, at Tokyo, Japan using a commercial grade u-blox GNSS receiver. According to the experiment result, the availability of positioning solution will increase with the aid GLONASS and QZSS. Introduction GPS provides accurate and reliable positioning/timing service for pedestrian application in open field environments. Unfortunately, its positioning performance in urban areas still has a lot of potential to improve due to signal blockages and reflections caused by tall buildings. The signal reflections can be divided into multipath and non-line-of-sight (NLOS) effects. Recently, to use 3D building model as aiding information to mitigate or exclude the multipath and NLOS effects has become a popular topic of study. In the beginning, the 3D map model is used to simulate the multipath effect to assess the single reflection environment of a city [1]. The metric of NLOS signal exclusion using an elevation-enhanced map, extracted from a 3D map, is developed and tested using real vehicular data [2]. An extended idea of identifying NLOS signals using infrared camera set at an automotive vehicle was suggested [3]. The potential of using a dynamic 3D map to design a multipath exclusion filter for a vehicle-based tightly-coupled GPS/INS integration system was studied in Obst et al. [4]. A forecast satellite visibility based on a 3D urban model to exclude NLOS signals in urban areas was developed in Peyraud et al. [5]. The above approaches aim to exclude the NLOS signal, however, the exclusion is very likely to cause a HDOP distortion scenario, due to the blockage of buildings along the two sides of streets. In other words, the lateral (cross direction) positioning error would be much larger than that of the along track direction. As a result, approaches that apply multipath and NLOS signals as measurements become essential. One of the most common methods, the shadow matching method, uses 3D building models to predict the satellite visibility and to compare it with measured satellite visibility to improve the cross street positioning accuracy [6]. A multipath and NLOS delay estimation based on software defined radio (SDR) and 3D surface model based on particle filter was proposed and tested in a static experiment in the Shinjuku area [7].The research team of The University of Tokyo developed a particle filter based positioning method using a 3D map to rectify the positioning result of commercial GPS single-frequency receiver for pedestrian applications [8]. The evaluation of the QZSS L1-submeter-class augmentation with integrity function (L1-SAIF) correction to the proposed pedestrian positioning method is also discussed in Hsu et al. [9]. However, the visible satellite in urban canyon using only GPS and QZSS would not be enough for the proposed method. The use of the emerging multi-GNSS, such as GLONASS, Galileo and Beidou, could be a potential solution to the situation of lack of visible satellites for the proposed method. The objective of this paper is to assess the performance of the proposed pedestrian positioning method using GPS, GLONASS and QZSS. 3D Building Models Construction and Ray-Tracing Technique This paper establishes a 3D building model by a 2D map that contained building location and height information of buildings from 3D point clouds data. The Fundamental Geospatial Data (FGD) of Japan, which provided by Japan geospatial information authority, is open to Japan public. This FGD data is employed as 2D geographic information system (GIS) data. Thus, the layouts and positions of every building on the map could be obtained from the 2D GIS data. In this paper, the 3D digital surface model (DSM) data is provided by Aero Asahi Corporation. Fig. 1 shows the process of constructing the 3D building model used in this paper. This paper firstly extracts the coordinates of every corner of buildings from FGD as shown in the left of Fig. 1. Afterwards, the 2D map is integrated with the height data from DSM with it. The right of Fig. 1 illustrates an example of a 3D building model that established in this paper. The developed 3D building map contains very small amount of data for each building in comparison to that of the 3D graphic application. This paper only contains the frame data of each building instead of the detail polygons data of building. This basic 3D building map is utilized in the simulation of ray-tracing. Fig. 1. The construction of the 3D building map from a 2D map and DSM. The ray tracing used in this paper is according to from the ray-tracing technique addressed in [10]. This paper does not consider diffractions or multiple reflections because these signals occurred under unfavorable conditions. Thus, this paper only utilizes the direct path and a single reflected path. The developed ray-tracing simulation can be used to distinguish reflected rays and to estimate the reflection delay distance. This paper assumes that the surfaces of buildings are reflective smooth planes, namely mirrors. Therefore, the rays in the simulation obey the laws of reflection. In real world, the roughness and the absorption of the reflective surface might cause the mismatch between the ray-tracing simulation and the real propagation. This paper neglects this effect due to the roughness of the building surface is much smaller compared to the propagation distance. The detail of the ray-tracing algorithm used can be found at [8]. 3D Map-Based Pedestrian Positioning Method The flowchart of the developed 3D city building model based particle filter is shown in Fig. 2. Fig. 2. The flowchart of the particle filter using 3D city building models. As shown in Fig.1, this method firstly implements a particle filter to distribute position candidates (particles) around the previous estimated and receiver estimated positions. In the Step 2, when a candidate position is given, the proposed method can evaluate whether each satellite is in LOS, multipath or NLOS, by applying the ray-tracing procedure with a 3D building model. According to the signal strength, namely carrier to noise ratio (C/N0), the satellite could be roughly classified into LOS, NLOS and multipath scenarios [8]. If the type of signal is consistent between C/N0 and ray-tracing classification, the simulate pseudorange of the satellite for the candidate will be calculated. In the LOS case, simulated pseudoranges can be estimated as the distance of the direct path between the satellite and the assumed position. In the multipath and NLOS cases, simulated pseudoranges can be estimated as the distance of the reflected path between the satellite and the candidate position via the building surface. Ideally, if the position of a candidate is located at the true position, the difference between the simulated and measured pseudoranges should be zero. In other words, the simulated and measured pseudoranges should be identical. Therefore, the likelihood of each valid candidate is evaluated based on Distribute Position Candidates (Particle) Determine Valid Satellites for each Candidate City 3D Building Models LOS/NLOS Classification based on C/N0 GNSS Ephemeris LOS/NLOS Classification based on Ray-Tracing and Candidate Position Calculate the Simulated Pseudorange for each Candidate Raw Pseudorange Measurement Ionospheric and Tropospheric models Calculate Pseudorange Similarity for each Candidate Receiver Estimated Position C/N0 Estimated by Receiver Calculate Weighted Average of all Candidates Step1
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