Zhao, Hongbo (Beihang University), Chen, Yiming (Beihang University), Yin, Zeqi (Beihang university), Hu, Shan (Beihang University)

On the Node Impact for Multi-Vehicle Cooperative Positioning Based on CRLB Analysis

Scheduled for presentation during the Regular Session "Collective perception and localization" (ThAT4), Thursday, September 26, 2024, 11:50−12:10, Salon 7

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurate and reliable vehicle positioning has been widely recognized as an important issue in intelligent transportation systems (ITS). Multi-vehicle cooperative positioning (CP) is an effective method for improving the Global Navigation Satellite System (GNSS) positioning, especially in complex urban environments. Many researches have been conducted for estimating the CP performance, where the impact of the CP scale has been a vital issue in most of these researches. However, the impact of introducing a new vehicle into a system has not been analyzed rigorously in theory, especially when the measurements of this new vehicle are degraded. In this paper, we verify that introducing a new vehicle into the CP system will improve CP performance from the perspective of Cramer-Rao lower bound (CRLB) by a series of theoretical analyses. Meanwhile, we also point out that the actual CP result may degrade when we introduce a vehicle with large measurement noises, this difference in CRLB and CP result might be caused by the inaccurate estimation of the weighting matrix. Numerical simulation and field tests are conducted and the results verify our findings.