# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 6 Field Data Analysis
### Land Vehicle Test  
In this third and final test, the same receiver was mounted on a vehicle and data was collected in the different road environments shown in Figure 16. The length of the moving average was empirically reduced to 30 s due to the dynamic characteristics of the data and consequently faster multipath variations. The \(N\) and \(M\) were chosen as 5 and 3 to satisfy the theoretical false alarm probability of 0.2 \(\times\) 10\({}^{-8}\). As mentioned previously, a cycle slip detection procedure was performed based on the phase velocity trend method and the corresponding threshold was set to 3 cycles due to higher dynamics (as discussed by [36]). Similar to the pedestrian test, only GPS L1 positioning is analyzed where only L1 pseudoranges are used in the position solution, but measurements on other frequencies are used for monitoring purposes. The data was categorized into three groups for low-multipath environment, campus multipath canyon (surrounded by buildings with smooth surfaces as shown in Figure 16b) and downtown Calgary (Figure 16c). In the first two data sets, due to good satellite visibility, consecutive exclusion of detected measurements was used to improve the position solution. In the downtown area, due to low visibility, iterative change of measurement weights (de-weighting approach) was used to keep all the measurements, but to iteratively de-weight those affected and reduce multipath errors. Referring to Section 4.2, a linear function was used to de-weight detected measurements and the maximum number of iterations was set to 100, observed enough to reach the predefined geometry threshold. In all cases, the PDOP threshold was again set to 8. Similar to the pedestrian test, the combination of monitoring techniques was performed by first excluding or de-weighting detected measurements (using all detection metrics) and then correcting the multipath errors on the included (or not de-weighted) PRNs by applying the CMC-based error correction. Figure 15: Positioning RMS errors and 3D improvement percentages for GPS L1—Pedestrian test scenario and GPS L1 positioning. Position results are presented in Table 3 and Figure 17, they are extracted over the intervals the position solution has converged on. While position results have significantly improved in the campus multipath environment, no improvement is observed in the downtown area. The latter case is because the CMC-based error correction has low performance due to the multiplicity of cycle-slips caused by obstructions and antenna motion in the downtown test. As discussed in Section 3.1, the carrier phase measurements are affected by cycle slips resulting in a new unknown ambiguity. Due to the dependency of the CMC on carrier phase measurements, there is a need to restart the time averaging process in the event of a cycle slip. Therefore, the multiplicity of cycle slips limits the performance of the moving average and thus the correction approach. Moreover, investigation of all data sets revealed that the exclusion or de-weighting of distorted measurements breakdown when more than 50% of measurements are distorted. This was the case in downtown for most of the epochs. It was also observed that a higher dynamic in multipath environments, like the vehicular test scenario in downtown, results in lower performance due to the lag imposed by the time-averaging process. In this case, since multipath behaves more like noise (i.e., stochastic errors) than bias, the C/N\({}_{0}\)-based stochastic model of measurements and weighting scheme shows slightly higher performance before applying exclusion or correction approaches. Figure 16: Data collection for land vehicle test—Setup and multipath environments, (**a**) test vehicle (**b**) campus multipath canyon and (**c**) downtown Calgary. Comparing all test scenarios, it is observed that each monitoring technique improves positioning performance (except for the downtown test) and each has its own limitations.