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Sensors 2016, 16(6), 755; doi:10.3390/s16060755

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

1
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2
Key Laboratory of Technology on Intelligent Transportation Systems Ministry of Transport, Research Institute of Highway Ministry of Transport, Beijing 100088, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 4 April 2016 / Revised: 9 May 2016 / Accepted: 20 May 2016 / Published: 25 May 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
View Full-Text   |   Download PDF [6636 KB, uploaded 25 May 2016]   |  

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. View Full-Text
Keywords: vehicle positioning; distributed-dual-H∞ filtering; reduced inertial sensor system; generalized regression neural network vehicle positioning; distributed-dual-H∞ filtering; reduced inertial sensor system; generalized regression neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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