In this paper, we propose a reliable hybrid positioning methodology by combining the advantages of \mbox{H}\infty filter and extreme learning machine (ELM), which addresses GPS outages and uncertain nonlinear drift of MEMS INS simultaneously. A novel parallel-dual-\mbox{H}\infty filtering (PDHF) mechanism is proposed to prevent the \mbox{H}\infty filter from diverging during GPS outages and to make full use of supplementary observations. The PDHF is composed of an enhanced \mbox{H}\infty filter and an auxiliary \mbox{H}\infty filter. The enhanced \mbox{H}\infty 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 \mbox{H}\infty filter only fuses the supplementary observations. Furthermore, an ELM module with good generalization ability is designed and augmented with the auxiliary \mbox{H}\infty filter to constitute a "virtual enhanced \mbox{H}\infty filter." In the case of GPS outages, the "virtual enhanced \mbox{H}\infty filter" provides accurate corrections for stand-alone INS. Due to the characteristics of the \mbox{H}\infty 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.
A Reliable Hybrid Positioning Methodology for Land Vehicles Using Low-Cost Sensors
IEEE transactions on intelligent transportation systems ; 17 , 3 ; 834-847
2016
Article (Journal)
English
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