# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 6 Field Data Analysis
### Static Test
#### 6.1.1 Monitoring Results
In this process, \(N\) and \(M\) were chosen 300 and 10 (sample of measurements with the input rate of 1 sample/s), respectively, to satisfy the overall probability of false alarm of 1.4 \(\times\) 10\({}^{-8}\). The same  
Figure 5: CMC-based monitoring metric for GPS L1 C/A (blue), L2C M + L (red), L51 + Q (green)—PRN 10 affected by different multipath levels. Figure 6: Left vertical axis: C/N\({}_{0}\) measurements for PRN 10—GPS L1 C/A (blue), L2C M + L (red), L5 I + Q (green)—Right vertical axis: Evolution of elevation angle over time for PRN 10 (purple). parameters were used for DC/N\({}_{0}\) and GF-based detection metrics defined by Equations (12) and (13). Figures 8 and 9 show the DC/N\({}_{0}\) and GF-based detection metrics and the corresponding results based on the differences between L1, L2C and L5 GPS signals. For GPS L1 positioning, detection results include those provided by the single-frequency C/N\({}_{0}\)-based detection metric on L1 and DC/N\({}_{0}\) and GF-based detection metrics using the L1-L2C and L1-L5 combinations. For L2C positioning, detection results are related to the single-frequency metric on L2C and the differential metrics using the L1-L2C and L5-L2C combinations. Similarly, for L5 positioning, detection results are considered based on the single-frequency metric on L5 and the differential metrics on L1-L5 and L5-L2C combinations. For each positioning system, a combination of all or some of the related results will be used based on the designed scenario. Figure 7: (**a**) Single-frequency C/N\({}_{0}\)-based detection metric and (**b**) _M of N_ detection outputs for GPS L1/L2C and L5 frequencies—PRN 10. Figure 8: (**a**) DC/N\({}_{0}\)-based detection metric and (**b**) _M of N_ detection outputs for GPS L1/L2C and L5 frequencies—PRN 10. Compared to the single-frequency C/N\({}_{0}\)-based detection metric, the detection sensitivity of the DC/N\({}_{0}\) and GF-based metrics have slightly increased, especially for the intervals when the multipath signature is magnified due to the differencing effect explained in Section 3.3. There are intervals in which the detection metrics are within the black boundaries but multipath is detected. This is a result of the \(M\) of \(N\) latency discussed in Section 4.1. Given the periodic nature of GNSS multipath, a side effect of the \(M\) of \(N\) latency is reducing the rate of up and down in detection results. In the case of exclusion, the lower change rate in detection outputs was observed to be beneficial to smooth position results, as intentionally considered in this research. Comparing detection results with the corresponding CMC values shown in Figure 5, it is observed that the detection output remains zero for low multipath  
Figure 9: (a) GF-based detection metric and (b) \(M\) of \(N\) detection outputs for GPS L1/L2C and L5 frequencies—PRN 10.  
measurements. When the multipath error exceeds 3 m, it is mostly detected by all the detection metrics. The detection sensitivity can be adjusted by tuning the detection thresholds and of course in a trade-off between detection and false alarm probabilities. This choice depends on the application and multipath environment. Herein, the detection results are used for measurement exclusion or de-weighting, which are in turn limited by geometry. Therefore, the detection sensitivity should be adopted such that relatively significant multipath errors are detected and the position solution improves by excluding or de-weighting the affected measurements. The sensitivity level of 3 m was examined and found as a proper value to sort out multipath affected measurements and benefit from excluding or de-weighting them under the desired PDOP threshold to be discussed in the next subsection.