Abstract
Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.
Highlights
The development of intelligent transportation is one of the essential objectives of building a smart city, namely smart mobility [1]
By considering the direct, reflected, diffracted, and multipath global navigation satellite system (GNSS) signal through ray-tracing, the measurement-level GNSS data with sophisticated modeling noises are simulated for multiple road agents in the urban area, in order to supply realistic large scale data for collaborative positioning (CP) research
The solutions of different positioning algorithms applied on the GNSS measurements from the proposed simulator ulator on different scenarios, including the least squares positioning method (LS), the least squares method with conon different scenarios, including the least squarespositioning positioning method (LS), squares method with consistency sistency check (CC), the ray-tracing method (RT), andthe the least
Summary
The development of intelligent transportation is one of the essential objectives of building a smart city, namely smart mobility [1]. The normal distribution can model many of the measurement noises, the GNSS measurement error in the urban environment is dominated by the enormous signal delay due to building reflection This delay is uniquely related to the geometry between satellites, GNSS receiver, and the reflecting surface, which may differ between each road agent. Based on the satellite ephemeris, 3D building model, and the location of different road agents, the GNSS measurement-level data corresponding to each agent is simulated through the ray-tracing algorithm, including pseudorange, C/N0 , and Doppler frequency. By considering the direct, reflected, diffracted, and multipath GNSS signal through ray-tracing, the measurement-level GNSS data with sophisticated modeling noises are simulated for multiple road agents in the urban area, in order to supply realistic large scale data for CP research.
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