|Abstract:||Google announced that raw GNSS measurements are available to apps in the Android N operating system, which means we can get pseudoranges, Dopplers and carrier phase from a phone or tablet through various APIs. Several studies show that pseudorange observations can provide meter-level accuracies, and phase measurements can potentially allow for a precise positioning. The process of reducing the code measurement noise should importantly precede to figure out the smart phone’s position within 1 or 2 meter as well as to make a set of cycle ambiguity candidate for the RTK or other precise positioning. In this paper, we introduces a suitable filtering algorithm which can be used to reduce the noise level of the single-frequency pseudorange from the Android N API without a severe divergence due to the ionospheric variation. The most general and convenient filtering method to smooth the single-frequency GNSS pseudorange is Hatch filter. The Hatch filter is a code-smoothing technique that uses the variation of the carrier phase. It can effectively reduce the noise of a pseudorange with a very simple filter construction, but it occasionally causes an ionosphere-induced error for low-lying satellites. To find out the optimal smoothing constant for the conventional Hatch filter, various algorithms have been recently suggested. It is difficult to estimate the ionospheric-error by SF (Single-Frequency) measurement for a single epoch, thus recently proposed methods focus on bounding the divergence due to the ionospheric error, rather than on reducing it. To minimize the effect of the ionosphere-induced error, we propose an SF divergence-free Hatch filter that can compensate for the divergence due to ionospheric variation by the grid ionospheric vertical error (GIVE) from the SBAS message type (MT) 26, which can be easily obtained from typical low-cost single-frequency GNSS receivers. Even though we assume that the SBAS has modelled current ionospheric delay sufficiently, it is necessary to consider the residual error due to the coarse resolution of the ionospheric vertical delay, 0.125 m. Considering the quantization error, we derived the error equation of the SF divergence-free Hatch filter, and found out the optimal smoothing width as a function of pseudorange CNMP (Code Noise and Multi-Path) and Obliquity factor. To verify the suggested SF divergence-free Hatch filter algorithm and to evaluate its performance in the Android N measurement, we had smoothed the raw measurement from Nexus 9 at the Sejong university, S. Korea and prevented a severe ionospheric divergence using the MT 26 information from the multi-functional satellite augmentation system (MSAS) of Japan. To apply the suggested divergence-free Hatch filter to the smart phone, we made a CNMP model function of the elevation angle which can fit the real CNMP errors of the Nexus 9. For the range-domain analysis, we did the double difference between Android device and Reference station at Sejong university, and then compared its residual errors with those of an U-blox receiver. The RMS (root mean square) of the Nexus 9 pseudorange noise error has been reduced from 5m to 0.6m for all the satellites, while the typical Hatch filter cannot prevent the divergence so that the RMS has risen up to 1.0m for the satellite of the low-elevation angle. Considering that current GNSS chipset supports only single-frequency measurement and is expected not to include multi-frequency receiving function, the suggested divergence-free Hatch filter is very practical to improve the performance while maintaining the recursive and simple form of the conventional Hatch filter.|
Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||188 - 225|
|Cite this article:||
Shin, Donghyun, Lim, Cheolsoon, Park, Byungwoon, Yun, Youngsun, Kim, Euiho, Kee, Changdon, "Single-Frequency Divergence-free Hatch Filter for the Android N GNSS Raw Measurements," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 188-225.
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