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| Content Provider | MDPI |
|---|---|
| Author | Li, Xu Xu, Qimin Li, Bin Song, Xianghui |
| 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. |
| File Size | 6795264 |
| File Format | |
| e-ISSN | 14248220 |
| DOI | 10.3390/s16060755 |
| Journal | Sensors |
| Issue Number | 6 |
| Volume Number | 16 |
| Language | English |
| Publisher Date | 2016-05-25 |
| Access Restriction | Open |
| Subject Keyword | vehicle positioning distributed-dual-H∞ filtering reduced inertial sensor system generalized regression neural network |
| Content Type | Text |
| Resource Type | Article |
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