Performance Analysis of Robust Cooperative Positioning Based on GPS/UWB Integration for Connected Autonomous Vehicles

The accurate position is a key requirement for autonomous vehicles. Although Global Navigation Satellite Systems (GNSS) are widely used in many applications, their performance is often disturbed, particularly in urban areas. Therefore, many studies consider multi-sensor integration and cooperative positioning (CP) approaches to provide additional degrees of freedom to address the shortcomings of GNSS. However, few studies adopted real-world datasets and internode ranging outliers within CP is left untouched, leading to unexpected challenges in practical applications. To address this, we propose a Robust Cooperative Positioning (RCP) scheme that augments the GPS with the Ultra-Wideband (UWB) system. A field experiment is conducted to generate a real-world dataset to evaluate the RCP scheme. Moreover, the analysis of the collected dataset enables us to optimise a simple but effective Robust Kalman Filter (RKF) to mitigate the influence of outlier measurements and improve the robustness of the proposed solution. Finally, a simulated dataset is derived from the real-world data to comprehensively study the performance of the proposed RCP method in urban canyon scenarios. Our results demonstrate that the proposed solution can crucially improve positioning performance when the number of visible GPS satellite is limited and is robust against various adverse effects in such environments.