Program

Abstract:

In recent years, more and more intelligent products based on Global Navigation Satellite Systems have been used in urban environments, and these autonomous systems rely heavily on accurate positioning information. At the same time Integrity monitoring is receiving more scholarly attention as an important safety indicator for positioning systems. The development of high precision sensors and the improvement of the computing performance of positioning terminals have made it possible to perform high precision positioning based on factor graph optimization. However, there are significant differences between the positioning system based on factor graph optimization and traditional single-point positioning systems, and there is still no efficient method for monitoring the integrity of such positioning systems. This article presents a new innovation-based fault detection and exclusion algorithm for the positioning framework based on factor graph optimization. Unlike snapshot-based integrity monitoring algorithms that require an initial position estimate at each moment, the proposed approach uses historical position data, current observations and equations of motion to monitor the integrity of satellite data at the current moment, which can effectively improve the real-time performance of the positioning system. Moreover, by taking full advantage of factor graph optimization framework, the satellite availability of the previous moment is judged during each moment of monitoring, increasing the success rate of the next moment of integrity monitoring and improving the overall availability of the system. The real-time and effectiveness of the proposed algorithm was verified using data from an Open-Sourced Multisensory Dataset.