A Reliable Hybrid Positioning Methodology for Land Vehicles Using Low-Cost Sensors
Qimin Xu, Xu Li, Bin Li, Xianghui Song, Zhixiang Cai · IEEE Transactions on Intelligent Transportation Systems · 2015
In this paper, we propose a reliable hybrid positioning methodology by combining the advantages of H∞filter and extreme learning machine (ELM), which addresses GPS outages and uncertain nonlinear drift of MEMS INS simultaneously. A novel parallel-dual-H∞filtering (PDHF) mechanism is proposed to prevent the H∞filter from diverging during GPS outages and to make full use of supplementary observations. The PDHF is composed of an enhanced H∞filter and an auxiliary H∞filter. The enhanced H∞filter is developed by fusing not only GPS information but also supplementary observations, which include yaw angle provided by electronic compass, longitudinal velocity derived from wheel speed sensor, and lateral velocity constrained by assumptions, whereas the auxiliary H∞filter only fuses the supplementary observations. Furthermore, an ELM module with good generalization ability is designed and augmented with the auxiliary H∞filter to constitute a “virtual enhanced H∞filter.” In the case of GPS outages, the “virtual enhanced H∞filter” provides accurate corrections for stand-alone INS. Due to the characteristics of the Hoc filter, the proposed methodology is immune to uncertain nonlinear drift of MEMS INS in nature. To verify the effectiveness of the proposed methodology, road-test experiments with various scenarios were performed. The experimental results indicate that the proposed methodology outperform all the compared counterparts.