## 5 Interfering Data Identification Model  
Since the above problems are based on the existing information to determine whether the signal has interference in the actual situation, the UWB simply records the sample data in the process of collecting the signal, and it cannot be judged, which may cause problems in the modeling process of large error. In order to reduce the error, a normal \"loofunction\" \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Normal Target Coordinates**} & \multicolumn{6}{c}{**Abnormal Target Coordinates**} \\ \hline
**Number** & **X (mm)** & **Y (mm)** & **Z (mm)** & **Number** & **X (mm)** & **Y (mm)** & **Z (mm)** \\ \hline
1 & 1156.134 & 664.323 & 1143.159 & 6 & 2083.379 & 717.629 & 747.945 \\ \hline
2 & 3166.684 & 1722.603 & 1135.261 & 7 & 4288.560 & 1640.898 & 945.884 \\ \hline
3 & 2722.536 & 1171.291 & 1233.503 & 8 & 1769.324 & 1267.567 & 2400.749 \\ \hline
4 & 2462.341 & 1009.229 & 1938.670 & 9 & 3560.707 & 2006.856 & 2242.047 \\ \hline
5 & 1480.031 & 2554.463 & 1731.295 & 10 & 4823.989 & 2074.467 & 1184.086 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Normal/abnormal target coordinate results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Normal Target Coordinates**} & \multicolumn{6}{c}{**Abnormal Target Coordinates**} \\ \hline
**Number** & **X (mm)** & **Y (mm)** & **Z (mm)** & **Number** & **X (mm)** & **Y (mm)** & **Z (mm)** \\ \hline
1 & 3688.821 & 2230.001 & 1285.947 & 6 & 4747.971 & 2107.551 & 1027.347 \\ \hline
2 & 4203.046 & 1748.947 & 1225.706 & 7 & 2482.486 & 1539.052 & 757.827 \\ \hline
3 & 3185.988 & 1745.961 & 1297.826 & 8 & 1730.915 & 1410.560 & 1847.548 \\ \hline
4 & 2591.112 & 1879.385 & 2099.195 & 9 & 2026.283 & 830.003 & 914.749 \\ \hline
5 & 511.498 & 8.201 & 1705.062 & 10 & 1207.771 & 836.135 & 2107.460 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ten sets of target coordinates. function containing four target points is used to solve each dataset, and a function value is obtained. Randomly remove one target point and solve the problem with the remaining three target points. You will get the coordinates of four intersection points. If the signal is disturbed, the four intersection points formed by the intersection of the spherical surfaces generated by the four anchor points produce a large deviation. Therefore, the threshold is determined according to the empirical formula. If the fluctuation range of these five values is less than the threshold, the data will be regarded as normal data. If the data fluctuation is greater than or equal to the threshold, this will indicate that there are problematic data. For normal distance data that is not disturbed, the coordinates of the four anchor points can be used to position the specific coordinates of a target point; if one anchor point is removed, the remaining three anchor points will calculate the coordinates of 4 close to the target point. A total of five coordinates can be derived from the coordinates of one anchor point. Here, a fluctuation level is defined to judge whether this set of data is disturbed. If the fluctuation level is lower than a given threshold, this means that this set of data is not disturbed; on the contrary, when the fluctuation level is higher than the given threshold, it means that the set of data is not disturbed. Data are disturbed. The formula for the volatility level is as follows:  
\[\text{Volatility level}=\frac{\text{Coordinate maximum}-\text{Coordinate minimum}}{\text{Mean coordinate}} \tag{11}\]  
According to the 10 groups of data in annex 4, a fluctuation level can be obtained by calculating each group. If the fluctuation range of these five values is small, the data will be regarded as normal data. If the data fluctuate greatly, it will mean that there are problematic data. \[\text{f(x)}=\left\{ \begin{aligned} &\text{normal, set to 0, \ fluctuation level}<\text{threshold}\\ &\text{Abnormal, set to 1, \ fluctuation level}\geq\text{ threshold}\end{aligned} \right.