A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 6; p. 755
• Main Authors: Li, Xu, Xu, Qimin, Li, Bin, Song, Xianghui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.05.2016; MDPI
• Subjects: Accelerometers; Accuracy; distributed-dual-H∞ filtering; Drift; generalized regression neural network; Global positioning systems; GPS; Inertial; Land; Mathematical models; Modules; Neural networks; Nonlinearity; reduced inertial sensor system; Sensors; vehicle positioning; Vehicles; China; vehicle positioning; reduced inertial sensor system; generalized regression neural network; distributed-dual-H∞ filtering
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16060755

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.