In urban environments, the location accuracy of autonomous vehicles and unmanned aerial vehicles (UAVs) using navigation systems may be degraded because the global navigation satellite system (GNSS) positioning accuracy is degraded owing to satellite visibility or multipath signals. Therefore, to ensure the safety of autonomous navigation systems, we need technologies that can recognize or predict changes in the GNSS observation environment when vehicles enter areas with severe reception conditions. In this study, we developed an algorithm determining the GPS signal shadowing range that can be blocked by nearby obstacles at a specific grid point. To evaluate the algorithm, we collected GPS signals in the test area and analyzed the signal characteristics by overlapping the satellite trajectory and satellite signal shadowing area modeling results. As a result of the experiment, GPS signals were received in the satellite invisible area owing to the multipath effect, but the data continuity was reduced in the corresponding satellites, and L2 signals were disconnected or the amplitude of the vibration of MP1 was significantly larger than that of the visible satellites. Therefore, the SEM generation algorithm is expected to improve the positioning accuracy of autonomous navigation systems by removing multipath signals and simulating the observation environment of the GNSS receiver.