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.
Highlights
The most widespread land vehicle positioning systems are those which integrate a GlobalPositioning System (GPS) and an Inertial Navigation System (INS)
It can be determined that the STD value is 62% smaller after H∞ filtering for pitch angle, while the magnitude determined that the STD value is 62% smaller after H∞ filtering for pitch angle, while the magnitude is 45% for roll angle
It can be determined that the STD value is 62% smaller after H8 filtering for pitch angle, while the magnitude is 45% for roll angle
Summary
The most widespread land vehicle positioning systems are those which integrate a Global. A highly reliable and cost-efficient vehicle positioning solution is proposed which integrates the information from MEMS-RISS, low-cost GPS, and supplementary sensors and 3sources. After eliminating the negative effect of uncertain nonlinear drift, the sensors and sources are composed of an electronic compass, a wheel speed sensor, and velocity pitch and roll angle are used in INS mechanization and as the inputs of GRNN module. A novel distributed-dual-H∞ filtering (DDHF) mechanism is adopted to ability to solve function approximation problem is specially designed forsensors the application of the address theany uncertain nonlinear drift and make full use of the supplementary and sources. A hybrid methodology which combines part of the this paper can be summarized as follows: DDHF mechanism and the GRNN module is adopted to compensate for RISS position errors during (1) GPS.
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