Name
Abstract | ||
|---|---|---|
Sidereal filtering is an effective method for mitigating multipath error in static GPS positioning. Using accurate estimates of multipath repeat time (MRT) in sidereal filtering can further improve the performance of the filter. There are three commonly used methods for estimating the MRT: Orbit Repeat Time Method (ORTM), Aspect Repeat Time Adjustment (ARTA), and Residual Correlation Method (RCM). This study utilizes advanced sidereal filtering (ASF) adopting the MRT estimates derived by the three methods to mitigate the multipath in observation domain, then evaluates the three methods in term of multipath reduction in both coordinate and observation domain. Normally, the differences between the MRT estimates from the three methods are less than 1.2 s on average. The three methods are basically identical in multipath reduction, with RCM being slightly better than the other two methods, whereas for a satellite affected by orbit maneuver (satellite number 13 in this study), the MRT estimated by the three methods differ by up to tens of seconds, and the RCM- and ARTA-derived MRT estimates are better than ORTM-derived ones for ASF multipath reduction. The RCM shows a slight advantage in multipath mitigation, while ORTM is the one of lowest computation and ARTA is the optimal one for real-time ASF. Thus, the best MRT estimation method for practical applications depends on which criterion overweighs the others. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/rs10020006 | REMOTE SENSING |
Keywords | Field | DocType |
GPS,multipath repeat time,orbit maneuver,single-differenced observable residual,advanced sidereal filtering | Multipath propagation,Computer vision,Residual,Sidereal time,Satellite,Algorithm,Filter (signal processing),Multipath mitigation,Global Positioning System,Artificial intelligence,Geology,Computation | Journal |
Volume | Issue | ISSN |
10 | 2 | 2072-4292 |
Citations | PageRank | References |
1 | 0.38 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghua Wang | 1 | 64 | 15.40 |
| Jiexian Wang | 2 | 16 | 9.45 |
| Danan Dong | 3 | 3 | 2.91 |
| Haojun Li | 4 | 3 | 2.11 |
| Ling Han | 5 | 1 | 0.38 |
| Wen Chen | 6 | 1 | 0.72 |