Performance Improvement of RTK-GNSS with IMU and Vehicle Speed Sensors in an Urban Environment

Abstract

The increasing demand for navigation and automation has led to the development of a number of accurate and precise navigation applications that make use of the Global Navigation Satellite System (GNSS) and additional sensors. One of the precise navigation techniques in GNSS, the real-time kinematic (RTK) technique, is well known. In this method, once the correct integer ambiguities are found in the carrier phase observation data, position can be determined to within 10cm. In particular, the advent of QZSS and BeiDou satellites can increase the availability of RTK-GNSS (relative to RTK using only GPS). It is understood that the increasing availability of RTK-GNSS will improve the performance of the integration of GNSS with additional sensors because the errors due to the inertial measurement unit (IMU) accumulate as time goes on. On the other hand, GNSS tends to suffer from multipath errors, especially in urban environments. To overcome this problem, a method was developed for improving RTK-GNSS using a low-cost IMU and conventional vehicle speed sensors. In this study, the quality of the complete observation data was assessed based on the carrier-to-noise ratio and satellite elevation angle, and the least-squares ambiguity decorrelation adjustment method and the ratio test were used to obtain fixed positions. We used speed information obtained from Doppler measurements as an alternative source of information; information from the IMU and vehicle speed sensor (integrated with the RTK-GNSS via a Kalman Filter) was used when there were no visible satellites. We also used the IMU and vehicle speed sensors to detect wrong fixes in the RTK-GNSS. A position and orientation system for land vehicles (Applanix) was used to estimate the reference positions. During GNSS outages, it is important to accurately determine the last heading of the car for precise navigation. In this study, it was found that GNSS Doppler-based direction data are required to obtain better direction information. The results of the experiment demonstrate that our proposed method is, to some extent, beneficial as an alternative to the conventional RTK-GPS in an urban environment.