Mechanical Engineering

Susu Fang1, Zengcai Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Jiacheng Fan1

1Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, College of Mechanical Engineering, Shandong University, Jinan, P.R. China

 

Received: April 9, 2018
Accepted: December 7, 2018
Publication Date: June 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201906_22(2).0009  

ABSTRACT

This paper details a new model for land vehicle navigation including attitude, velocity, and position estimation using an odometer (OD) and a magnetometer (MAG) in conjunction with strap down inertial navigation system (SINS) sensors. In this new model, the SINS/OD navigation system is designed firstly. In SINS/OD system, the application of non-holonomic constraints for obtaining virtual velocity measurements is investigated and an innovation-based estimation adaptive (IAE) Kalman filter is designed to provide a land vehicle navigation solution by combining the velocity measurement from an odometer and error in the centripetal acceleration difference as a new observable from SINS sensors. In addition, an adaptive federated Kalman filter (AFKF) is designed, which integrates the magnetometer on the basis of the SINS/OD navigation system. It is shown that the accuracy of the new method is higher than of SINS, and using it with the magnetometer in the integrated navigation system yields more accurate estimations. Finally, field tests show that the SINS/OD/MAG system improves the precision of the navigation system and promotes a fault-tolerant performance. The proposed approach can be used as a backup navigation system during outages of the Global Positioning System (GPS) for an extended amount of time with the navigation error noticeably bounded. The redundancy and integrity of the entire navigation system are also further enhanced.


Keywords: Navigation System Model, Vehicle Integrating Navigation, Kalman Filter, Fault-tolerant performance


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