## 6 Test and Analysis
The quadratic penalty factor \(\sigma\) affects the accuracy of the decomposition. The larger the penalty factor, the narrower the frequency band, and the more concentrated the reconstructed signal. From the collected waveform, it can be seen that except for the drone target signal, all other signals output by the sensor are noise. In this paper, the weighted average method of hard and soft threshold function is used to eliminate the influence of signal smoothness difference of single mode, and the processing effect is more ideal. After the signal reconstruction is used in the algorithm, the maximum point of the waveform is directly extracted through the peak feature point of the target signal, which reduces the time delay error and improves the test accuracy. Following the data fusion processing method for target coordinates on multi-acoustic basic arrays as described in the fifth section, we performed data fusion processing on the five UAV positioning experiments from Tables 1-3. The computed results are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{6}{c}{**Target Time Value of Acoustic Sensor in Basic Array A**} & \multirow{2}{*}{\(\mathbf{P(x_{A}\ ^{\prime},y_{A}\ ^{\prime},z_{A}\ ^{\prime})}\)} \\  & & \(t_{0}\) [ms] & \(t_{1}\) [ms] & \(t_{2}\) [ms] & \(t_{3}\) [ms] & \(t_{4}\) [ms] & \\ \hline
1 & 61.83 & 67.65 & 71.89 & 75.28 & 70.28 & 1.54, \(-\)0.25, 14.22 & 31.54, \(-\)0.25, 14.22 \\
2 & 89.87 & 86.42 & 88.56 & 86.09 & 84.03 & \(-\)0.28, \(-\)3.94, 18.26 & 29.72, \(-\)3.94, 18.26 \\
3 & 63.67 & 75.21 & 80.31 & 85.31 & 79.31 & 1.11, \(-\)0.10, 14.65 & 31.11, \(-\)0.10, 14.65 \\
4 & 81.21 & 90.43 & 95.76 & 90.65 & 86.01 & 0.03, \(-\)1.76, 13.81 & 30.03, \(-\)1.76, 13.81 \\
5 & 48.56 & 67.76 & 58.42 & 57.46 & 66.42 & 1.19, \(-\)0.86, 13.26 & 31.19, \(-\)0.86, 13.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test data in basic array A. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{6}{c}{**Target Time Value of Acoustic Sensor in Basic Array B**} \\ \cline{2-7}  & \(t_{0}\) [ms] & \(t_{1}\) [ms] & \(t_{2}\) [ms] & \(t_{3}\) [ms] & \(t_{4}\) [ms] & \\ \hline
1 & 339.94 & 331.68 & 340.62 & 349.26 & 339.59 & 100.97, \(-\)0.2, 8.95 & 50.97, \(-\)0.20, 8.95 \\
2 & 310.98 & 303.48 & 312.62 & 320.83 & 311.95 & \(-\)99.28, \(-\)3.94, 18.26 & 49.72, \(-\)3.94, 18.26 \\
3 & 327.05 & 319.23 & 328.10 & 336.69 & 328.07 & 101.11, \(-\)0.10, 14.65 & 51.11, \(-\)0.10, 14.65 \\
4 & 343.41 & 335.56 & 344.58 & 353.02 & 344.26 & 100.03, \(-\)1.76, 13.81 & 50.03, \(-\)1.76, 13.81 \\
5 & 315.67 & 307.76 & 316.69 & 325.25 & 316.55 & 101.19, \(-\)0.86, 13.26 & 51.19, \(-\)0.86, 13.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test data in basic array B. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{6}{c}{**Target Time Value of Acoustic Sensor in Basic Array B**} \\ \cline{2-7}  & \(t_{0}\) [ms] & \(t_{1}\) [ms] & \(t_{2}\) [ms] & \(t_{3}\) [ms] & \(t_{4}\) [ms] & \\ \hline
1 & 253.62 & 248.24 & 260.62 & 260.72 & 248.35 & 4.39, \(-\)54.35, 2.73 & 50.97, \(-\)0.20, 8.95 \\
2 & 234.50 & 230.43 & 242.48 & 242.01 & 229.89 & \(-\)0.28, \(-\)53.94, 18.26 & 49.72, \(-\)3.94, 18.26 \\
3 & 239.21 & 234.43 & 246.66 & 246.78 & 234.56 & 1.11, \(-\)50.10, 14.65 & 51.11, \(-\)0.10, 14.65 \\
4 & 242.24 & 237.53 & 249.77 & 249.57 & 237.32 & 0.03, \(-\)51.76, 13.81 & 50.03, \(-\)1.76, 13.81 \\
5 & 244.22 & 239.32 & 251.59 & 251.63 & 239.36 & 1.19, \(-\)50.86, 13.26 & 51.19, \(-\)0.86, 13.26 \\ \hline \hline \end{tabular}
\end{table}