## 5 Field Data Analysis
### Static Test Scenario  
GPS L1 C/A data was collected using a static rooftop antenna (NovAtel GPS-703-GGG) with one reflector in its vicinity to test a short-delay multipath scenario as shown in Figure 7a. In the first step, GPS data was collected for three days successively and analyzed by a PROPAK-V3 NovAtel receiver to monitor the effect of multipath on different PRNs during their full periods. This step was performed to find the appropriate data collection interval based on the repeatability of the multipath effect on a static antenna. In the next step, for the selected time interval, IF samples were collected at the same location for half an hour. The satellite skylot is shown in Figure 7b for this interval. Some of the satellites, including PRN 17, have higher elevation angles and are expected to be less affected by multipath. PRN 30 has the lowest elevation and is more vulnerable to multipath. In this step, the data was collected using the same antenna (NovAtel GPS-703-GGG), down-converted and sampled using a NI (National Instruments) PXIe-1065 sampling front-end with a 10 MHz sampling frequency. The IF signals were then acquired and tracked in a software receiver providing correlator outputs to extract SQM results for different PRNs. The software receiver used is a modified version of [33] designed in Matlab. In this procedure, a narrow correlator discriminator with 0.2 chips early-late spacing was implemented. Monitoring correlators were located 1 chip apart; the coherent integration time was 20 ms.  
Figure 7: (**a**) Multipath data collection for static test scenario; (**b**) GPS satellites skylot for static data collection. Figure 8 shows Carrier-to-Noise-density ratio (C/N\({}_{0}\)) and Code-Minus-Carrier phase (CMC) measurements for a sample of PRNs. The C/N\({}_{0}\) values are computed based on a classic technique known as the Narrowband-Wideband Power Ratio (NWPR) method [34]. CMC is a well-known technique to characterize and measure code multipath errors [35]. To this end, carrier phase measurements are subtracted from the corresponding pseudorange measurements. The results include code and carrier noise and multipath errors, carrier phase ambiguities and twice the ionospheric errors (due to ionospheric code delay and phase advance). The effects of ambiguity and ionosphere are estimated and removed using polynomial curve fitting. Neglecting the effect of carrier phase noise and multipath errors, the resulting value will be a measure of code multipath and noise. Figure 9 shows monitoring results for the Double Delta metric defined in Table 1. Similar results, not presented here, were observed for Ratio and Delta metrics. In Figure 9, the Double-Delta metric outputs were calibrated and normalized using its theoretical variance according to Table 1. In this normalization, the C/N\({}_{0}\) values were smoothed using a moving average window of 60 s. Afterwards, detection thresholds were fixed to \(\pm 3\) times the Standard Deviation (SD) for the normalized metrics. The _M of N_ detection strategy was used with \(N=500\) samples (or 10 s) and \(M=12\), 15 and 20, the overall probability of false alarm would be \(2\times 10^{-8},~{}1.6\times 10^{-11}\) and \(1.8\times 10^{-14}\) respectively. As shown in Figure 9a, the output of the designed detection algorithm for relatively clean PRN 17 is zero for all epochs, identifying this PRN as a clean measurement. The detection output for PRN 15 and 30 are shown in Figure 9b,c, respectively. It is observed that, while PRN 15 with medium multipath (according to its CMC measurement) remains almost undetected, PRN 30 is detected as the affected measurement for the intervals with strong multipath. It is also observed that the aforementioned decision procedure detects multipath with a delay related to the length of the moving window in the detection strategy. Figure 8: C/N\({}_{0}\) and CMC measurements for three types of PRNs: PRN 17 with low multipath (clean PRN), PRN 15 with medium multipath and PRN 30 with high multipath.