## 2 Main Subject
### Proposed Sensor Fusion Technique for Active Driving Assistance  
Position estimation using a single UWB sensor results in a strong error due to the obstacles and the reception strength of radio waves. Therefore, the direction also has a strong error in estimating the direction through the position estimated based on the UWB sensor. Since this is difficult to use as a direction estimation for active driving assistance, the direction is estimated with the fusion technology based on the yaw angular velocity data and the direction data calculated from the UWB sensor and complementary filter on the IMU sensor. Based on this, this study proposes an EKF sensor-based fusion method with the UWB sensor-based position estimation data by performing dead reckoning on the encoder sensor. Figure 6 represents a schematic diagram of the proposed sensor fusion technique for active driving, showing the process of estimating the direction and position using the fusion of UWB/encoder/IMU sensors. The sensor fusion technique for active driving assistance proposed in this study is largely carried out in three processes. First, the direction of the wheelchair is calculated based on the complementary filter. After that, the amount of the wheelchair X-axis position change, \(\Delta X_{EN}\), and the amount of the Y-axis position change, \(\Delta Y_{EN}\), are calculated through the calculated direction and encoder sensor data. Finally, the position of the wheelchair is calculated by fusing the calculated position change and the UWB position data. There are two problems when calculating the angle based on the angular velocity value of the IMU. First of all, since the angle calculated by accumulating the angular velocity as an absolute angle cannot be obtained at present, only the difference between the angle of the starting point and the measurement point can be calculated if the angle of the wheelchair at the starting point is not known. The second is the drift error caused by accumulating angular velocity values to measure the angle [44]. Therefore, in this study, the angle of the wheelchair calculated from the UWB sensor and the angle of the wheelchair measured from the IMU sensor are fused using Equation (4) in order to solve these two problems. The data measured from the UWB sensor include high noise, but there is no drift error because it is not a cumulative calculation. The acceleration data measured from the IMU sensor have less noise, but drift error occurs. In order to implement the fusion  
Figure 6: Schematic diagram of the proposed sensor fusion for active driving assistance. based on the characteristics of these two data types, this study proposes a method using a complementary filter. The complementary filter is usually used when the two data types have mutually secure frequencies, and the UWB sensor data have low-frequency noise, and the angular velocity obtained with the IMU sensor have high-frequency noise. Since each data type is fused through filters of different frequencies using the complementary filter, the UWB and IMU sensor data with different characteristics are fused and utilized. As shown in Equation (8), the UWB sensor data and IMU angular velocity sensor data can be fused. Based on the weight of the complementary filter, \(\beta\), the angular velocity \(\theta_{UWB}\) measured from UWB and the angular velocity \(\Delta\theta_{Gyro(t)}\) of the yaw axis measured from IMU are fused to calculate the angle of the current wheelchair, \(\theta_{t}\). Based on this, Equation (7) is implemented to calculate the amount of \(\Delta X_{EN}\) and Y-axis position change, \(\Delta Y_{EN}\), of the wheelchair. \[\theta_{t}=(1-\beta)\Big{(}\Delta\theta_{Gyro(t)}\times\Delta t+\theta_{t-1} \Big{)}+\beta\times\theta_{UWB} \tag{8}\]  
EKF is an algorithm that calculates the covariance of sensors through past and new measurement data and estimates the results by fusing each sensor according to the system model.