The continuous modernisation of existing Global Navigation Satellite Systems (GNSS) and the development of new systems with a multitude of different carrier frequencies and a variety of signal modulations creates a true multi-GNSS and multi-signal environment available today.
Still most precise GNSS processing strategies rely on dual-frequency measurements only by applying the Ionosphere-Free (IF) Linear Combination (LC) of GNSS observables and therefore do not benefit from the available multi-signal environment. While in this processing approach the first order effect of the ionospheric delay can be eliminated almost completely, the formation of linear combinations of GNSS observables leads to a noise increase for the resulting observations and a loss of some of the physical characteristics of the original signals, like the integer nature of the carrier phase ambiguity.
In order to benefit from the multi-GNSS and multi-signal environment available today, the scientific analyses and precise applications presented in this work are based on the raw observation processing approach, which makes use of the original (raw) observations without forming any linear combinations or differences of GNSS observables. This processing strategy provides the flexibility to make use of all or a selection of available multi-GNSS and multi-signal raw observations, which are jointly processed in a single adjustment as there is no inherent limitation on the number of usable signals. The renunciation of linear combinations and observation differences preserves the physical characteristics of individual signals and implies that multi-signal biases and ionospheric delays need to be properly determined or corrected in the parameter estimation process.
The raw observation processing approach is used in this work to jointly process measurements from up to three different GNSS, including eleven signals tracked on up to eight different carrier frequencies in one single adjustment.
The bias handling for multi-GNSS and multi-signal applications is analysed with a focus on physically meaningful parameter estimates to demonstrate the benefits of handling clock offset parameters, multi-signal code biases and ionospheric delay estimates in a physically meaningful and consistent way. In this context, receiver-specific multi-GNSS and multisignal biases are analysed and calibrated by the use of a GNSS signal simulator. The disadvantages of eliminating physical characteristics due to the formation of linear combinations of observations or commonly used parameter estimation strategies are demonstrated and discussed.
The carrier phase Integer Ambiguity Resolution (IAR) approach developed and implemented in the course of this work is based on the joint processing of multi-GNSS and multi-signal raw observations without forming any linear combinations or observation differences. Details of the implemented IAR approach are described and the performance is analysed for available carrier signal frequencies of different GNSS. Achieved results are compared to the conventional IAR approach based on IF linear combinations and the so called Widelane (WL) and Narrowlane (NL) ambiguities. In addition, the resolution of inter-system integer ambiguities is analysed for common GNSS signal frequencies.
The performance of the implemented IAR approach is demonstrated and analysed by the joint Precise Orbit Determination (POD) of multi-GNSS satellites based on fixed multi-frequency carrier phase ambiguities. The improvement of the satellite orbit and clock quality by fixing raw observation ambiguities confirms the successful implementation of the IAR approach based on raw observation processing. Multi-GNSS satellite orbits and clock offsets determined with this approach are compared to results generated with the conventional IF linear combination processing approach and independent external products. This comparison demonstrates an at least equivalent performance of the implemented IAR approach based on raw observation processing. In addition, the fixed raw observation ambiguities are used to investigate and discuss characteristics of multi-GNSS and multi-frequency phase biases
