Advanced Sidereal Filtering for Mitigating Multipath Effects in GNSS Short Baseline Positioning

Abstract

Advanced sidereal filtering (ASF) is an observation-domain sidereal filtering that adopts the repeat time of each individual satellite separately rather than the mean repeat time, adopted by the modified sidereal filtering (MSF). To evaluate the performance of ASF, we apply the method to filter the multipath for a short baseline based on a dual-antenna Global Navigation Satellite System (GNSS) receiver. The errors from satellite and receiver clocks, satellite orbit, troposphere, ionosphere, and antenna phase center variations are greatly eliminated by single difference between the two antennas because they are connected to the same receiver clock. The performances of ASF are compared with MSF to evaluate the gain for multipath mitigation. Comparisons indicate that ASF slightly outperforms MSF when the repeat time values of all satellites incorporated in data processing are within the normal range (86,145-86,165 s), but the difference of variance reduction rate between ASF and MSF is statistically significant. When the data of a satellite with repeat time outside the normal range are included, the performances of MSF become much worse, but ASF is almost not affected. This advantage of ASF over MSF is important because the proportion of the days on which at least one satellite's repeat time exceeds the normal range reaches 71.19% based on the statistics on the data of 2014 and 2015. After applying ASF multipath corrections on the test days, the averages of standard deviations of north, east, and up component are reduced from 3.8 to 2.1 mm, 3.2 to 1.7 mm, and 7.6 to 4.3 mm, respectively. Comparison between applying ASF with the single-day model and with the seven-day model indicates that the former is generally more effective in multipath reduction.

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

“…Therefore, observation domain SF has better potential than coordinate domain SF. Because the repeat periods of each satellite are slightly different, the optimal method for implementing SF is to model the multipath separately for each satellite; this is called advanced SF (ASF) [25]. Based on the 'zero mean' assumption, Zhong et al and Ye et al constructed multipath models of the ASF method by converting the DD residuals into single differential (SD) residuals [26,27].…”

Extracting relevant patterns from GNSS observations to mitigate multipath in RTK deformation monitoring

“…Currently, in the field of high-precision static observation, real-time multipath mitigation methods based on carrier phase positioning mainly include multipath hemispherical map (MHM) (Fuhrmann et al 2014;Dong et al 2015) and sidereal filtering (SF) (Zhong et al 2009;Atkins and Ziebart 2015;Chang et al 2018;Wang et al 2018) methods based on prior residuals, as well as prior reflector identification methods based on precise modeling of the near-field environment (Lau and Cross 2007;Zimmermann et al 2017;Zimmermann et al 2018). The SF method first notices the consistency of the satellite orbit over a long period of time and the unchanging near-field environment around the GNSS antenna.…”

GNSS multipath suppression technology based on postcorrelation and independent component analysis

“…These methods are mainly used in data postprocessing, which is difficult to apply in real-time positioning. The data processing method also includes the method developed from the time domain repetition period based on the satellite constellation, such as sidereal filtering (SF) [ 9 ] and advanced sidereal filtering (ASF) [ 10 ], this kind of method has difficulties such as large amount of modeling and complicated correction; and the modeling method based on multipath spatial repeatability such as multipath hemispherical map (MHM) [ 11 ], trend surface analysis-based multipath hemispherical map (T-MHM) [ 12 ] and single-difference multipath hemispherical map (SD-MHM) [ 13 ]. In addition, with the development of neural networks and machine learning, there are many new methods to identify line of sight (LOS) and non line of sight (NLOS) signals, including NLOS multipath classification of GNSS signal correlation output using machine learning [ 14 ], machine learning based LOS/NLOS classifier for GNSS shadow matching [ 15 ], neural networks based GPS spoofing detection [ 16 ].…”