Enhancement of heading accuracy for GPS/INS by employing average velocity in low dynamic situations

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Choi, Min Jun, Kim, Yong Hun, Kim, Eun Ju, Song, Jin Woo
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Automobile vehicles navigation; average velocity measurements; Doppler effect; Doppler shift; Global Positioning System; Global positioning systems; GPS; GPS integrated navigation; heading accuracy; Inertial coordinates; Inertial navigation; Kalman filters; Mathematical analysis; Navigation systems; Performance degradation; Position measurement; Range errors; Receivers; Satellite navigation systems; Time lag; Vehicle dynamics; Velocity; Velocity measurement
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2977675

In this paper, we proposed an algorithm that improves the heading accuracy of the global positioning system (GPS)/inertial navigation system (INS) integrated system used in automobiles by manipulating INS velocity and GPS velocity measurements. Two velocities are provided by the GPS receiver: the velocity calculated using the position difference and the velocity calculated using the Doppler shift. The velocity obtained using the position difference is an average velocity for a certain time period, which is inaccurate under dynamic conditions because of its time delay. In contrast, the velocity from the Doppler shift is an instantaneous velocity and has no time delay. However, it also relies on pseudo-range error and noise, which degrades the heading estimation accuracy in low dynamic situations. Thus, although the velocity measurements can improve heading accuracy, the navigation performance is often degraded when the velocity measurements are used for GPS/INS integration. To improve the heading accuracy by solving the aforementioned problems, we proposed a heading accuracy improvement algorithm that employs the average velocity measurements obtained using the averaged GPS velocity and the average velocity of the INS. Since the proposed average velocity measurements are calculated using long baseline, the proposed algorithm can improve the heading accuracy without using other sensors, especially in the case of low dynamic situations. It can be easily applied to the existing GPS/INS integrated system, making it suitable for use in automotive navigation systems. In this research, it is verified that the average velocity measurements can be substituted for Kalman filter measurements, and the performance improvements are confirmed through simulations and experiments.