GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

Abstract

Accurate localization of road agents 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 are 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 experiment requiring numbers of devices is hard to be conducted, 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, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. 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.

Scalability and Latency Analysis of the Centralized 3D Mapping Aided GNSS-Based Collaborative Positioning

“…Since the GNSS measurements suffer complicated interferences from the buildings in the urban canyon, it is essential to employ a sophisticated GNSS measurement simulator to obtain the measurements with realistic error behaviors. Therefore, we employ our developed GNSS RUMS [12] to simulate realistic GNSS measurements for the performance analysis of the collaborative positioning algorithm. The overall flowchart of the employed simulator is given in Fig.…”

“…After applying different simulation approaches, the GNSS 𝐶/𝑁 0 , interfered ranging measurement 𝑠, and the Doppler shift ∆𝑓 are obtained, which will be further combined with the receiver-parameter-related systematic errors and noises as the final simulated GNSS measurements. The detailed simulation steps can be found in [12]. The simulated GNSS measurements of multiple road agents will be applied with the 3DMA GNSS collaborative positioning algorithm to evaluate the positioning performance.…”

“…Firstly, the trajectories of multiple road agents in the urban canyon are simulated based on the SUMO (Simulation of Urban MObility) [11] to consider the dynamic behavior of vehicular or pedestrian agents. Based on the trajectory of each road agent, the corresponding GNSS measurements, including the carrier-to-noise ratio (𝐶/𝑁 0 ), pseudorange, and Doppler frequency, are simulated by the GNSS realistic urban multi-agent simulator (GNSS RUMS) [12] developed by our team. During the GNSS measurement simulation, the measurement degradation from buildings is searched using the ray-tracing technique, and further simulated by the GNSS-reflectometry (GNSS-R) technique [13] and the uniform geometrical theory of diffraction (UTD) [14] for the reflection effect and the diffraction effect, respectively.…”

Research on collaborative positioning of intelligent vehicle aided navigation based on computer vision technology

“…Under this positioning method, it fully considers the interference of urban complex environment on GPS signals, and uses a new road sign sensor with Bluetooth technology as a data receiving device [3][4] , which solves the disadvantage that traditional dead reckoning stage can only carry out short-time high-precision navigation, and effectively handles the problem of "blind area" of urban vehicle navigation. Moreover, in the design process, the adaptive combined Kalman filter algorithm is used to fuse multi-sensor information, so that the vehicle navigation and positioning error can be stabilized within 10.0m [5][6] . However, this method requires high signal transmission, and the corresponding positioning results are difficult to meet the objective requirements of navigation and positioning in terms of accuracy for areas with strong signal interference or complex vehicle driving environment.…”