## 2 Main Subject
### Proposed Position Estimation System for Active Driving Assistance
#### 2.2.1 UWB-Based Object Estimation Technique  
The data collected from the UWB module have high noise and may include strong outlier data due to the influence of multiple paths caused by diffraction, reflection, etc. of radio waves due to unexpected obstacles [40]. Therefore, data pre-processing is needed to remove noise and outliers for implementing precise UWB sensor-based position estimation. Noise and outliers are removed through the data pre-processing process presented in this study. Then, the position and direction angle are estimated through the position estimation and direction angle estimation algorithm considering the outdoor getting on/off space. The data collected from the UWB sensor generally include high outliers and strong noise due to the noise generated from the multiple paths caused by diffraction and reflection of radio waves because of obstacles and the intensity of radio reception. Therefore, since the collected UWB sensor data show strong outliers and high noise, errors in the Kalman filter covariance calculation and prediction process can be added to the sensor fusion system through the Kalman filter. For this reason, pre-processing is required. Therefore, various filter theories are used to remove noise from such data, and representative methods include average filter, median filter, and exponential smoothing filter [41; 42; 43]. The average filter reduces noise through the principle of averaging after data collection, but it is not suitable for use in the pre-processing of the UWB sensor because it calculates the average including the outliers of the UWB sensor. The median filter collects a large number of data and uses intermediate values to remove noise. However, there is a problem that various measured values other than outliers are cancelled as intermediate values because of the nature of the median filter, even though there is a strong advantage in the outliers. In this study, since the UWB sensor was used to estimate the position of a moving wheelchair, the median filter, which may cancel the data obtained during movement, is not suitable for sensor pre-processing. In the case of the exponential smoothing filter, it is a filter that accumulates and calculates the previous value and the current value with a constant index. However, it is not strong against outliers like the previous average filter. In addition, since this is a filter that is calculated by continuously accumulating the previous values, it is affected by outliers and delays in the previous values. Therefore, it is not suitable as a pre-processing process for the UWB sensor. Figure 2 shows the data pre-processing flow chart proposed in this study, and when data are input, the data are first selected and stored based on \(Q_{buffer}\) with n sizes. Then, the stored data are copied to \(S_{buffer}\) to calculate the median value. In order to remove strong outliers and noise in \(S_{buffer}\), data that do not have noise greater than or equal to a weight \(\alpha\) in the median value are stored in \(M_{buffer}\). Then, the average is calculated based on the data remaining in \(M_{buffer}\). Therefore, since the average is derived by removing outlier values of a certain value or more after calculating the intermediate value for each data, the disadvantage of the average including outliers of the average filter can be removed and the average of the cancelled value of the median filter can be calculated. For this reason, this study proposes this pre-processing for the UWB sensor data. The position estimation system using the UWB sensor basically consists of a number of fixed nodes (ANs, anchor nodes) and mobile nodes (TNs, tag nodes) that identify the installation position accurately. In general, the method of estimating the position using the UWB sensor is implemented using a triangulation method, and three straight distances are required for this.