|Abstract:||Android has been providing global navigation satellite system (GNSS) raw measurement data since the release of the Android operating system Nougat (N) in 2016. Users can use the GNSS measurements to directly estimate their accurate location. Studies are being actively conducted to improve the accuracy of user locations down to the decimeter level using GNSS raw measurements in Android smartphones and by applying methods such as Kalman filter (KF) and real-time kinematics (RTK). Android smartphones use planar inverted-F antennas (PIFAs) owing to hardware limitations. The PIFA structure is susceptible to multipath errors; therefore, significant noise is introduced into the raw GNSS measurements. In addition, there are software limitations. Further, the Duty Cycle functions in Android smartphones prioritize communication, which is the primary function of smartphones, over navigation during processor allocations. When a Duty Cycle is enabled, the antenna tracking the satellite to receive carrier measurements is forcibly disconnected and repeatedly reconnected. However, studies have shown that cycle slips still occur, even after limiting the Duty Cycle function, owing to the limitations of the smartphone antenna, and the utilization of carrier measurements is still challenging. In addition, not every smartphone manufacturer offers seamless support for carrier measurement. Therefore, Conducting RTK research using carrier measurements of smartphones is difficult. Previous studies have addressed the limitations of smartphones by receiving satellite signals using high-performance antennas on roofs of high-rise buildings, in ideal environments, and rebroadcasting the signals to smartphones from rebroadcasting rooms. In contrast to carrier measurements, Doppler measurements are not affected by cycle slips. As shown in Fig. 1, Doppler measurements in Android smartphones are less accurate than carrier measurements in the absence of cycle slips. However, the accuracy of Doppler measurements is excellent compared to the accuracy of code measurements. In this study, we applied a code–Doppler smoothing method that combines the advantages of code and Doppler measurements in Android smartphones. Fields such as flight formation, autonomous driving, and car navigation require accurate distance between objects. Therefore, sensors that estimate distance, such as LiDAR and radar, are typically required. The relative positioning method can be used to accurately estimate the distance between objects using only satellite measurements without these sensors. In this method, the difference between the two pseudoranges is determined. Subsequently, an accurate relative distance between two receivers can be estimated using this baseline vector and the position of the satellite. In our previous study, we improved the distance estimation accuracy using raw GNSS measurements from an Android smartphone and the two methods previously mentioned. The methods were verified by conducting an experiment using receivers of different performance levels in static and dynamic environments, but not in an ideal environment such as the rooftop of a high-rise building. In this study, relative positioning accuracy was improved by using the extended KF and GNSS raw measurements from an Android smartphone. Furthermore, the code–Doppler smoothing method was applied based on the characteristics of smartphone measurements. This method of precision relative positioning using Android smartphones is anticipated to be useful because of its simple and convenient usage without the need for additional equipment.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||2849 - 2859|
|Cite this article:||
Park, Dana, Jang, JinHyeok, Woo, Jiyeon, Sung, SangKyung, Lee, Young Jae, "Precise Relative Positioning Using the CodeDoppler Smoothing Method and the Extended Kalman Filter of Android Smartphone," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 2849-2859.
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