GLONASS FDMA data for RTK positioning: a five-system analysis

Abstract

The use of the GLONASS legacy signals for real-time kinematic positioning is considered. Due to the FDMA multiplexing scheme, the conventional CDMA observation model has to be modified to restore the integer estimability of the ambiguities. This modification has a strong impact on positioning capabilities. In particular, the ambiguity resolution performance of this model is clearly weaker than for CDMA systems, so that fast and reliable full ambiguity resolution is usually not feasible for standalone GLONASS, and adding GLONASS data in a multi-GNSS approach can reduce the ambiguity resolution performance of the combined model. Partial ambiguity resolution was demonstrated to be a suitable tool to overcome this weakness (Teunissen in GPS Solut 23(4):100, 2019). We provide an exhaustive formal analysis of the positioning precision and ambiguity resolution capabilities for short, medium, and long baselines in a multi-GNSS environment with GPS, Galileo, BeiDou, QZSS, and GLONASS. Simulations are used to show that with a difference test-based partial ambiguity resolution method, adding GLONASS data improves the positioning performance in all considered cases. Real data from different baselines are used to verify these findings. When using all five available systems, instantaneous centimeter-level positioning is possible on an 88.5 km baseline with the ionosphere weighted model, and on average, only 3.27 epochs are required for a long baseline with the ionosphere float model, thereby enabling near instantaneous solutions.

A Multi-Frequency Galileo PPP-RTK Convergence Analysis with an Emphasis on the Role of Frequency Spacing

“…This fast convergence to the centimeter level is of great importance, as it implies that a single-receiver user can achieve such rapid positioning results without the use of a precise ionosphere model and, more importantly, without the need for a dense reference network to provide regional atmospheric corrections. With the above real data results, we believe that more studies can be considered for achieving near-instantaneous PPP-RTK positioning by incorporating GLONASS, BeiDou and QZSS data, similar to the five-system analysis of Brack et al [14].…”

“…With the current proliferation of GNSS systems, the availability of more satellites paves the way for further improvements to PPP-RTK ambiguity resolution and positioning based on the ionosphere-float model, i.e., the model which parameterizes the ionospheric delays as completely unknown parameters. In general, combining systems brings an improved satellite geometry that translates into reduced convergence times, as was demonstrated for GPS and BeiDou [7], GPS and Galileo [8,9], three-system GPS, Galileo and BeiDou [10,11], four-system GPS, Galileo, BeiDou and GLONASS [12,13], and five-system GPS, Galileo, BeiDou, GLONASS and QZSS [14].…”

GLONASS–only FDMA+CDMA RTK: Performance and outlook

“…They showed that for both standalone GLO-NASS and combined GLONASS+GPS, faster convergence time and more precise positioning solutions can be obtained once the GLONASS FDMA integer-estimable ambiguities are fixed. In Brack (2020) and Brack et al (2020), the new FDMA model was analyzed for multi-GNSS RTK positioning, considering short to long baselines. They proposed a partial ambiguity resolution approach with which the inclusion of the GLONASS integer-estimable FDMA model in multi-GNSS RTK positioning was beneficial under all considered cases.…”

A mean-squared-error condition for weighting ionospheric delays in GNSS baselines

“…Ionosphere-weighted GNSS parameter estimation is a popular and very flexible technique for strengthening estimator performance in the presence of ionospheric model delays. It is used in a wide variety of GNSS applications, ranging from the use of external ionospheric models (Schaer 1999;Memarzadeh 2009;Feltens et al 2011) to the incorporation of ionospheric corrections from reference networks, for instance for PPP or PPP-RTK (Odijk 2000;Paziewski 2016;Tomaszewski et al 2020;Teunissen 2021), and the strengthening of medium to long baseline models (Teunissen 1997;Bock 1998;Odolinski and Teunissen 2017;Brack et al 2021). However, no clear and provable rules have been established that specify the needed weighting in dependence on ionospheric circumstances.…”