## 4 Experiment and Analysis
### Equipment and Solutions  
In this study, the inertial navigation components and computational equipment used in the experiments were all integrated within the TNAV100 flight control computer, which is a flight control and navigation system specifically designed for unmanned aerial vehicles. The system internally integrates key components, such as a three-axis gyroscope, a three-axis accelerometer, and a GNSS module. In particular, the IMU employs an ADIS16470 series MEMS-grade IMU, and the GNSS module is equipped with the UM982 receiver chip from UNICORECOMM company. Additionally, we set the data sampling frequencies for the IMU and the GNSS receiver to 200 Hz and 1 Hz, respectively. To ensure the precise  
Figure 2: The WQOSA execution flowchart, where N represents the current total number of visible satellites, and K represents the intended number to be selected. comparison of error values in the experimental results, this experiment used a navigation system equipped with a high-precision fiber optic inertial navigation component, the PN133, as a reference. The output results of this system will serve as the benchmark reference values for navigation results based on MEMS inertial systems. Figure 3 shows a photograph of the equipment used in this experiment. Tables 1 and 2 detail the specific parameters of the inertial sensors of the experimental and reference systems for further technical comparison and analysis. and turning, reaching a maximum operating speed of nearly 120 km/h, and experimental data recording lasted for about 70 min. During the experiment, the vehicle's travel trajectory was meticulously recorded, as shown in Figure 4a, which illustrates the changes in satellite visibility throughout the entire experimental process. When driving through special road sections, the satellite observation conditions became adverse due to obstruction by buildings and terrain, leading to significant fluctuations in the number of visible satellites. This phenomenon was meticulously recorded during the experiment and is presented in Figure 4b in the form of a satellite count data graph, reflecting the challenges faced by satellite navigation systems in complex urban environments. This study aims to delve into the dynamic characteristics of attitude, velocity, and position errors during the vehicle's travel. Figure 5a presents a data graph showing the performance of the loosely coupled (LC) and tightly coupled (TC) integrated navigation algorithms in terms of attitude angle errors in the horizontal and vertical directions. This graph reflects the performance differences between the two algorithms in maintaining the accuracy of vehicle attitude estimation. Figure 5b provides statistics on the velocity errors of the LC and TC algorithms in the east, north, and vertical directions, demonstrating the specific impact of different coupling strategies on the accuracy of velocity estimation. Finally, Figure 6 details the positional errors of the LC and TC algorithms in the three spatial dimensions of longitude, latitude, and altitude, showcasing the performance of the two algorithms in terms of positioning accuracy. Figure 4: **(a)** A satellite map of the vehicle’s travel trajectory and (**b**) the variation curves of observable satellites and HDOP during the experiment. Figure 5: Performance comparison between LC and TC schemes. **(a)** Comparison chart of attitude errors in horizontal and vertical directions. **(b)** Comparison chart of velocity errors in eastward, northward, and vertical directions. Figure 4b shows that there is a relatively long period of GNSS denial from 1500 s to 1700 s into the experiment. During this period, the navigation error results significantly increase. In addition to this, the experimental data also contain several short periods of GNSS denial.