|Abstract:||Among the common RTK methods, instantaneous method and filter method are two popular ways to estimate ambiguities, and the later one is often used in surveying because of its high fixing-rate and reliability. However, in urban environment, traditional filters used for RTK where ambiguities are solved as filter states often fail to work due to the frequent satellite changes and cycle slips, degrading the performance of urban RTK. Based on the above considerations, we still apply an algorithm that uses filter but eliminate ambiguities from filter states and solve the AR problem in position domain to avoid the problem caused by satellite changes and cycle slips. To realize the idea and improve the execution efficiency, we propose an algorithm using adaptive Point Mass Filter (PMF) with wide-lane measurements to implement RTK. The main principle of the proposed algorithm is to use a set of grids with weights in position domain to approximate the posterior density. By recursively updating the grid weights with the use of the integer property of ambiguities, we can filter the trajectory and compress the searching space of position domain. The ambiguities we use to calculate the final position are achieved in an average weighted form based on the grid weights. Once the filter begins to converge, the average weighted ambiguities will get close to the correct integer values, thus providing a more accuracy and reliable positioning result in urban area. The results and analyses of actual data for both static and dynamic experiments are presented at the end of the paper.|
Proceedings of the 2016 International Technical Meeting of The Institute of Navigation
January 25 - 28, 2016
Hyatt Regency Monterey
|Pages:||846 - 857|
|Cite this article:||
Li, Wenyi, Cui, Xiaowei, Lu, Mingquan, "Urban RTK Using Adaptive Point Mass Filter with Wide-Lane Measurements," Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2016, pp. 846-857.
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