A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning
AbstractIn 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. View Full-Text
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Li, X.; Xu, Q.; Li, B.; Song, X. A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning. Sensors 2016, 16, 755.
Li X, Xu Q, Li B, Song X. A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning. Sensors. 2016; 16(6):755.Chicago/Turabian Style
Li, Xu; Xu, Qimin; Li, Bin; Song, Xianghui. 2016. "A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning." Sensors 16, no. 6: 755.
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