## 2 Main Subject
### Proposed Position Estimation System for Active Driving Assistance  
The single UWB sensor-based position and direction estimation method has difficulty correcting data distortion caused by obstacles during driving, and delays occur when noiseis strongly suppressed to increase the accuracy. For this reason, it is difficult to use as a driving assistance system, and in the case of dead reckoning such as IMU/encoder, it generates data in the form of a driving record rather than initial position identification. This makes it difficult to apply it to position and direction estimation because it causes a drift error because of initial position identification and data accumulation. Therefore, this study proposes a system as shown in Figure 1 in order to perform the position and direction estimation for driving assistance technology development. Noise in the UWB sensor data is removed through the data pre-processing process proposed in this study to suppress strong noise. The active driving assistance algorithm to the waypoint proposed in this paper requires direction and position information for active driving from the current position of the wheelchair. For this purpose, the yaw value of the IMU sensor data and the direction data from the UWB sensor are combined in the form of a complementary filter to estimate the direction information. The reason for this is that the real-time orientation information estimation of the wheelchair has an error due to the slip of the wheelchair's encoder information, so the error can be improved by estimating the yaw value of the IMU sensor, and the drift error caused by the yaw integration process can be improved by the position-change amount calculated with the UWB. The position information estimation can basically use the position value estimated by the UWB sensor. However, the distance error of the UWB sensor system can be improved by installing many sensor nodes, but the type proposed in this paper has an error range of 10-30 cm, and it is difficult to estimate the change according to the amount of instantaneous rotation of the wheelchair, so we propose a method to update the distance information to the waypoint by fusing the change amount of the IMU sensor and the encoder sensor attached to the wheelchair with the EKF algorithm. Among the methods for fusing sensor data, the complementary filter-based sensor fusion, AI-based sensor data fusion method, Kalman filter, and particle filter-based sensor data fusion method are representatively used [34; 35; 36; 37]. In the case of complementary filters, sensors are fused through mutual compensation between each data source, and they are utilized when the characteristics of each sensor are clear. AI-based sensor fusion mainly designs neural networks by learning from large amounts of data when it is difficult to define the characteristics of data collected from sensors [38]. Therefore, they are effective in deriving results through probabilistic decisions based on large databases, mainly in object recognition and estimation or inference with unclear features. However, AI-based fusion methods are computationally intensive when processing data compared to other algorithms and require a lot of data for training. The Kalman filter is a sensor fusion algorithm that fuses each sensor based on a system model and iteratively performs state prediction and measurement updates to estimate results based on real-time sensor data. Particle filters update their measurements by iterating through resampling and prediction based on probability. Figure 1: System diagram proposed in this study. Particle filters have the advantage of less computation compared to AI algorithms, but since they perform fusion based on probability, they require a larger amount of sensor data to be resampled compared to the Kalman filter algorithm. The use of particle filters compared to Kalman filters is not suitable for the use of UWB/IMU/encoder sensors in this paper.