## 5 Field Data Analysis
### Kinematic Scenario  
The sensitivity of the SQM approach for multipath detection was also examined in a kinematic scenario. Figure 10a shows the data collection environment. The building on the right has smooth surfaces which are considered as strong reflective sources. The GPS L1 data was collected with an antenna mounted on the test vehicle (Figure 10b) driving at a velocity of 1 m/s in the west direction through the multipath canyon shown in the figure. The data was down-converted and sampled using a Fraunhofer front-end with a 20 MHz sampling frequency. The GPS signals are then acquired and tracked in the software receiver (using a narrow correlator discriminator with 0.2 chips early-late correlator spacing) to monitor the signal quality as well as SQM metric outputs.  
Figures 11 and 12 show the monitoring results of PRN 21 during the different segments of the trajectory. Similar results were observed for other PRNs. The kinematic data collection was started in a fairly open sky environment whose first 5 s is shown in the figures. The vehicle then passed through  
Figure 10: (**a**) Kinematic data collection—Multipath canyon—University of Calgary; (**b**) Test vehicle and NovAtel antenna for GPS data collection.  
the multipath canyon for 100 s and ended in a fairly open sky environment (the last 35 s). The expected levels of multipath, corresponding to the vehicle trajectory, were observed in the resulting CMC measurements shown in Figure 11. In this plot, segments with relatively strong multipath are marked in red while medium and low multipath segments are marked in yellow and green, respectively.  
Figure 12 shows the SQM and detection results for PRN 21. In addition to the Double-Delta metric, the monitoring results are shown for the Ratio and Delta metrics as defined in Table 1. The coherent integration time was 20 ms. All SQM metrics were calibrated and normalized using corresponding theoretical variances listed in Table 1. In this normalization, the C/N\({}_{0}\) values were smoothed using a moving average window. Compared to the static scenario, the length of the moving average was reduced to 4 s due to the dynamic characteristic of the data and consequently faster multipath variations as a function of time. The detection thresholds were then fixed to \(\pm 3\) times the normalized standard deviation (SD). The decision was based on the _M of N_ detection strategy described above. Again, due to the dynamic data characteristics, the length of the sliding search window (N) was also reduced to 100 samples (2 s). According to Equation (7), \(M=7\), 9 and 11 were chosen and the overall false alarm probabilities are about \(1.3\times 10^{-8},\ 1.2\times 10^{-11}\) and \(1\times 10^{-14}\), respectively. The detection output for the regions with low multipath (green region shown in Figure 11) is zero, indicating clean range measurement epochs for PRN 21. It is also shown that the medium effect of multipath (multipath with less than 3 m ranging error according to CMC measurement) is buried under SQM metric noise and remains almost undetected. However, for those epochs strongly affected by multipath (when multipath error is in the order of 5 m or more), the SQM metrics is sensitive and the detection output shows the occurrence of multipath.  
Figure 11: C/N\({}_{0}\) and CMC measurements for PRN 21 (as a clear example of different levels of multipath during different segments of the trajectory).  
Figure 12: Signal quality monitoring and corresponding detection outputs for PRN 21—Dynamic scenario; (**a**) Normalized (single-sided or asymmetric) Ratio metric; (**b**) Normalized Delta metric; (**c**) Normalized Double-Delta metric.