JACIII Vol.23 No.3 pp. 427-436
doi: 10.20965/jaciii.2019.p0427


Improving GNSS Navigation and Control with Electronic Compass in Unmanned System

Xi Han*,**,***, Xiaolin Zhang*, and Yuansheng Liu**,***

*School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics
No.37 Xueyuan Road, Haidian District, Beijing 100083, China

**College of Robotics, Beijing Union University
No.97 Beisihuan East Road, Chaoyang District, Beijing 100101, China

***Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University
No.4 Gongti North Road, Chaoyang District, Beijing 100027, China

March 19, 2018
October 30, 2018
May 20, 2019
GNSS, electronic compass, BP-PID, neural network, unmanned system
Improving GNSS Navigation and Control with Electronic Compass in Unmanned System

GNSS course angle compensation with EC

This paper proposes a compensation technique for the global navigation satellite system (GNSS)/real-time kinematic (RTK) course angle data using an electronic compass for an unmanned system. Additionally, the proportion, integral, and derivative control based on a back-propagation neural network (BP-PID) is introduced to improve the steering safety and riding comfort. The course angle jitter was determined. Because the GNSS/RTK receiver cannot offer stable heading data under specific conditions, including but not limited to susceptibility to obstacles, complex electromagnetic environment, and fewer satellites. The compensation algorithm is based on the determination of the GNSS course angle variance ratio and the asynchronous characteristic between the GNSS and an electronic compass. The combined data provide accurate and robust navigation information for an outdoor unmanned system. To address the limitation of the in-system parameter adjustment, a back-propagation (BP) neural network is adhibited to a conventional proportion, integral, and derivative (PID) lateral control system. The BP-PID control module updates the incremental PID parameters through self-learning, and results in the smoother operation of the vehicle. The flowchart of the learning algorithm and method of calculating the parameters are presented. A typical measurement was conducted and the obtained results were compared with typical RTK navigation results. Thus, the effectiveness of the proposed compensation method was confirmed.

Cite this article as:
X. Han, X. Zhang, and Y. Liu, “Improving GNSS Navigation and Control with Electronic Compass in Unmanned System,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 427-436, 2019.
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Last updated on Sep. 19, 2019