## 3 Experiments and Results
### Configuring the Experimental Environment  
#### 3.1.1 Experimental Environment  
Three experimental environments were configured for quantitative verification of the position estimation algorithm for driving assistance proposed in this study. First of all, since the UWB sensor collects data containing strong outliers and noise, they require pre-processing. In order to compare the distance data before and after the application of the pre-processing algorithm proposed in this study, the experimental environment was configured as shown in Figure 7. An environment of a straight space without obstacles was created, and for performance evaluation, distance data before and after pre-processing were compared by applying one AN and TN. As shown in Figure 7, it is composed of a straight distance space without obstacles to proceed with the experiment at 1m intervals from 1 m to 5 m.  
This study proposes a UWB sensor-based direction estimation technology to assist in active driving to the destination of an intelligent wheelchair. However, since the direction estimation is less accurate with only a single UWB sensor, this study proposed a sensor fusion technology based on a complementary filter for the direction data from UWB and IMU sensors. The improved UWB direction estimation technique proposed in this study is a method of fusing the direction estimation data using the UWB sensor and the IMU sensor yaw angular velocity data proposed in this study using a complementary filter. Therefore, the second experimental environment, as shown in Figure 8, was configured for quantitative evaluation of the proposed complementary filter-based sensor fusion algorithm. The direction data were collected through an intelligent wheelchair platform, and an experimental environment was constructed to compare direction estimation according to the UWB sensor position and UWB/IMU sensor data using a complementary filter.  
Figure 7: Experimental environment for comparison before and after UWB data pre-processing. (**a**) Distance measurement experimental schematic plot; (**b**) Distance measuring experimental environment.  
The experimental environment was configured to check the direction estimation accuracy compared to the actual position by mounting a camera at the bottom of the wheelchair through rotation at a predetermined position, as shown in Figure 8.  
Finally, for active driving assistance to the destination of an intelligent wheelchair, this study proposed a UWB sensor-based position estimation technology. However, since the position estimation using a single UWB sensor is less accurate, a method of fusion based on the UWB/IMU/encoder sensors was proposed. Therefore, for the quantitative evaluation of the sensor fusion position estimation algorithm proposed in this study, an experiment was conducted through an experimental environment as shown in Figure 9. The experimental environment was configured to compare the results of the position estimation through an algorithm that suggests moving the designated path by attaching ANs to both ends of the door, just as ANs are attached to both ends of a narrow entrance door of a hospital or building. Two ANs were mounted at a height of 200 cm at 350 cm intervals, and the driving path was 200 cm wide and 300 cm long. For quantitative comparison, a camera was mounted at the bottom of the wheelchair to compare the actual position data with the position data estimated in the experiment to confirm its accuracy.  
Figure 8: Direction angle measurement data collection environment. (**a**) Angle measurement experimental schematic plot; (**b**) angle measurement experimental environment.  
Figure 9: Position estimation experimental environment. (**a**) Overall experimental environment for position estimation experiment; (**b**) experimental environment (200 cm by 300 cm) for position estimation experiment.