De-correlation of tropospheric error and height component on GNSS using combined zenith-dependent parameter
For high precision GNSS positioning, the troposphere is one of the most problematic error sources. Typically, the effect is minimal due to the spatio-temporal correlation when the baseline length is short enough in the relative positioning scenario. When a strong tropospheric anomaly effect is present, the problem can be much more complicated and the resultant positioning solution is typically no longer precise even for a baseline of a few kilometres in length. As the troposphere delay and height estimates are almost linearly correlated above a 20° elevation angle, the problem exists of how to de-correlate these two parameters to avoid such ill-conditioned cases. To obtain reliable height estimates, and avoid ill-conditioned cases, a new method is proposed in this dissertation: these two common zenith dependent parameters are combined into a single parameter plus weighting parameters. Once the parameters are combined and corresponding weighting parameters are determined, the vertical component can be retrieved. The feasibility of the methodology is investigated in a kinematic situation. To determine the weighting coefficient in this case, the residuals in a least-square estimator are analyzed. As the residuals can be decomposed into two different realms, either troposphere or ionosphere, the magnitude of the residual contribution of the troposphere for each satellite pair in the double difference can be determined. This value is further used to determine the weighting parameters. Through this new method, the common zenith-dependent parameters are found to be de-correlated. A number of data sets are processed and the results are analyzed, especially during severe inhomogeneous tropospheric conditions and under humid environments. In summary, in a kinematic scenario, the achievement is shown to be up to 20% (4 cm to 3 cm rms) with processed data. Compared to the conventional approach, the degradation of the vertical component during an anomalous weather period is almost eliminated in kinematic scenarios, which is the main goal of the research described in this dissertation. This means that this new approach is resistant to an anomalous tropospheric event.