## 4 Results and Discussion  
The environment chosen is representative of various conditions that may appear in urban and suburban scenarios. In particular, path segments where signal attenuation and multipath were observed are highlighted in Figure 7.  
In total, five satellites are acquired during the test campaign. The sky plot for the acquired satellites is shown in Figure 8. Satellite vehicle (SV) 9, SV 12 and SV 27 have good and similar elevation while the others are evenly distributed but mostly at low elevation. In fact, for SV 14 and SV 25, there is strong signal attenuation all along the path at most of the time. The carrier to noise ratio (\(C/N_{0}\)) for one of the satellites in view is shown in Figure 9 (SV 9). The trend shows a good availability of \(C/N_{0}\) for some time intervals, while for others either signal blockage or strong attenuation is experienced, as it can be noted for the time intervals from 100 to 300 s and from 520 to 840 s. The \(C/N_{0}\) drop is common to all the satellites during such intervals, and this reveals that the attenuation is due to obstacles close to the receiving antenna, such as the presence of foliage of the trees on the side of the street [16]. Figure 7: Path segment with close buildings and trees. In order to confirm the previous inference, the CADLL was used to analyze the GNSS signals in such intervals. Despite the drop in the \(C/N_{0}\), there were no multiple reflections detected in such time intervals  
Figure 8: Sky plot for the acquired satellites. Figure 9: Estimated \(C/N_{0}\) behavior for SV 9.  
of data collection. It has to be remarked that CADLL considers only multipath that is relevant in terms of distortion of the tracking curve (see Equations (1) and (2)). When only unit 0 is activated, if multipath is present, its effect is irrelevant for the GNSS receiver in terms of bias, even if they may have increased the noise floor and thus reduced the \(C/N_{0}\). The final conclusion is then that, in such time intervals, the \(C/N_{0}\) drop must be due to diffused multipaths and scattering (which could actually be generated by the foliage or by the building edges), but no specular multipath is present. However, processing the full set of data collected by means of the CADLL, segments of the path where specular multipath is present are found. Due to the dynamic data collection, the propagation conditions change quickly and the specular multipath is detected for short durations. As an example, multipath is detected on SV 9 at 475 s, for a duration of about 12 s.  
Figure 10 shows the trend of the normalized amplitude for the detected direct and multipath rays starting at time epoch 475 s. ALL0 and ALL1 are estimating the amplitudes of the LOS and multipath signals respectively. It is noted that the multipath amplitude is quite low with respect to the LOS amplitude, showing that the multipath is a very weak. In the figure, green and red lines are the respective I and Q components of the complex signal. For this case, the average estimated power ratio between the reflected ray and the LOS is \(-19\) dB. Figure 11 depicts the delay trend between the direct and the multipath signal. The average delay between the two signals is estimated to be about 196.5 ns. It is important to note that for this duration, the multipath is close to the direct signal. Also the trend is fairly steady and without significant changes, thus making likely that it is reflected from the same source during the whole time interval. The estimated delay in this case corresponds to a difference in traveling path of about 58 m. This estimation is in good agreement with the data collection performed by the LiDAR.