title: A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning.
abstract: In this paper, we propose a novel positioning solution for land vehicles which is highly reliable and cost-efficient. The proposed positioning system fuses information from the MEMS-based reduced inertial sensor system (RISS) which consists of one vertical gyroscope and two horizontal accelerometers, low-cost GPS, and supplementary sensors and sources. First, pitch and roll angle are accurately estimated based on a vehicle kinematic model. Meanwhile, the negative effect of the uncertain nonlinear drift of MEMS inertial sensors is eliminated by an H∞ filter. Further, a distributed-dual-H∞ filtering (DDHF) mechanism is adopted to address the uncertain nonlinear drift of the MEMS-RISS and make full use of the supplementary sensors and sources. The DDHF is composed of a main H∞ filter (MHF) and an auxiliary H∞ filter (AHF). Finally, a generalized regression neural network (GRNN) module with good approximation capability is specially designed for the MEMS-RISS. A hybrid methodology which combines the GRNN module and the AHF is utilized to compensate for RISS position errors during GPS outages. To verify the effectiveness of the proposed solution, road-test experiments with various scenarios were performed. The experimental results illustrate that the proposed system can achieve accurate and reliable positioning for land vehicles.
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. lixu.mail@163.com.
authors.forename: Xu
authors.initials: X
authors.lastname: Li
authors.affiliation: School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. jimmy.xqm@gmail.com.
authors.forename: Qimin
authors.initials: Q
authors.lastname: Xu
authors.affiliation: Key Laboratory of Technology on Intelligent Transportation Systems Ministry of Transport, Research Institute of Highway Ministry of Transport, Beijing 100088, China. libin@itsc.cn.
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.affiliation: Key Laboratory of Technology on Intelligent Transportation Systems Ministry of Transport, Research Institute of Highway Ministry of Transport, Beijing 100088, China. sxh@itsc.cn.
authors.forename: Xianghui
authors.initials: X
authors.lastname: Song
keywords: distributed-dual-H∞ filtering; generalized regression neural network; reduced inertial sensor system; vehicle positioning