# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 4 Performance Assessment and Validation  
### Experiment Setup  
GPS and Beidou system data provided in the \"UrbanNav\" dataset are adopted in this study [40]. The Tsim Sha Tsui and Whampoa subsets are chosen as training data for deep learning, while another GNSS dataset from Kowloon Bay (depicted in Figure 5) with similar collection procedures is used as testing data and to validate the GNSS/INS integration. Especially, data from three commercial GNSS receivers, including U-Blox F9P, Xiaomi 8, and NovAtel Flexpak6 (noted as U-Blox, Xiaomi, and NovAtel later in this paper), are considered and utilized as the representatives of different level receivers. Additionally, a separate ground truth trajectory is given by NovAtel SPAN-CPT after inertial explorer (IE) post-processing for reference. As for the solution uncertainty parameters of each receiver, fixed uncertainty and min uncertainty are set based on the typical positioning performance of the receivers or data-processing experience. Based on that, max uncertainty is chosen at a value higher than two times the fixed uncertainty, indicating a situation of extremely large uncertainty. Details on such settings are presented in Table 1.  
Apart from the GNSS receivers, the Xsens MTI-10 device and the smartphone built-in IMU device are chosen as the sensors that provide INS data for this study. The Allan Variance method [45] showcases the characteristics of the noise process and is used for IMU error analysis. It is applied in this study to obtain the INS bias and noise parameters for both the accelerometer and gyroscope [46], as summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **U-Blox** & **Xiaomi** & **NovAtel** \\ \hline Fixed uncertainty [m] & 30 & 60 & 20 \\ Max uncertainty [m] & 65 & 130 & 45 \\ Min uncertainty [m] & 1.5 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GNSS solution uncertainty settings.  
Figure 4: Illustration of the adaptive uncertainty mechanism.  
This study analyses an almost completely end-to-end car trajectory. The whole duration is 920 s, but different receivers might have a different total number of available GNSS epochs. As depicted in Figure 5, four sections, with the labels and estimated durations 1-2 (1-255 s), 2-3 (256-605 s), 3-4 (606-770 s), and 4-5 (771-920 s), constitute the whole trajectory in an anticlockwise manner. Section 1-2 has tall buildings on both sides, while section 4-5 has one side with tall buildings and another side with an open-sky environment. Similar surroundings happen in section 2-3 and 3-4, where buildings are on both sides with less density than section 1-2. Points A, B, and C denote junctions where the car stops for some time, and they happen roughly in the time periods 110-130, 290-350, and 630-730 (in second), respectively. Additionally, the 920 s trajectory passes through various typical urban scenarios for positioning. Figure 5 highlights three types of urban environments: (1) light urban: region with quasi-open-sky environment or with open-sky view on one side of the street; (2) medium urban: region surrounded by buildings with height of less than 40 m; (3) harsh urban: region surrounded by buildings taller than 40 m or with streets less than 10 m in width.