## 3 Data Collection and Methodology  
A field data collection campaign was carried out at Dortmund, Germany. The test scenario was selected so that the test vehicle followed a pre-defined trajectory along a 1.7 km long closed path. As previously remarked, the objective of the work is to assess the presence of obstacles generating multipath reflections using both GPS and LiDAR observables. The path shown in Figure 3 covers an urban environment, with two lane streets and 4-storey buildings. The buildings fronts are mainly planes parallel with respect to the axis of the streets. Some lampposts and trees are also present on the side of the streets. Figure 3 shows the reconstructed trajectory based on the GPS measurement performed by a professional receiver. Green dots indicate good GPS visibility and measurements accuracy, while red dots correspond to areas in which GPS quality is degraded due to poor visibility and multipath.  
Figure 4(a) shows the LiDAR sensors equipment that was used in the test campaign while Figure 4(b) shows the optimum orientation of 2 LiDAR sensors. More details on the equipment used to carry out the test can be found in [11, 12, 13].  
The block diagram shown in Figure 5 describes the methodology to perform the test. The LiDAR measurements and GPS raw measurements (in-phase and quadrature samples of the received signal at intermediate frequency) from the output of antenna front end are recorded at the same GPS time.  
Figure 2: CADLL monitoring unit.  
The LiDAR measurements are post-processed in order to extract the information regarding planes of buildings fronts. The raw output of the LiDAR is the so-called cloud of points represented by azimuth and elevation with respect to the phase center of the LiDAR, of the source of reflection. By processing the cloud of points, information like shape, orientation of planes and size of objects can be obtained. For this analysis, planes have been reconstructed by the cloud of points and their distances from the receiver are the features extracted from the LiDAR cloud of points.  
Figure 4: (**a**) Optech Lynx LiDAR sensors equipment; (**b**) Optimum orientation of 2 LiDAR sensors.  
Figure 3: Field data collection path.  
GPS raw data are post-processed using CADLL to estimate the multipath present in the received signal. CADLL estimates the delays and amplitudes of multipath, which are further translated into the distances of the reflector generator with respect to the center of phase of the GNSS antenna. The information from both the LiDAR feature extraction and GNSS multipath estimation is then compared to check the correlation between the two measurements (see Figure 6).  
Figure 5: Block diagram of the process.  
Figure 6: Data processing flow chart.  
It has to be remarked that GPS and LiDAR observables are collected in a synchronized manner, by using a common reference clock. Furthermore, the LiDAR+CADLL system is tuned in order to exclude delays larger than one chip duration (and that would not affect the pseudorange measurement), in order to reduce the computational time of the CADLL. In fact, it has to be remarked that in principle, CADLL is able to track components with larger delay, as proved in [14, 15].  
Only if the LiDAR distances fall within the threshold (300 m), the CADLL operation starts, initializing the two starting units. Unit 0 tracks the direct signal while unit 1 tracks multipath signal if there is any.