## 6 Test and Analysis

Table 3: Test data in basic array C.  
According to the data of Table 4, we found that although the measurement object is the same target, the measurement results of each acoustic basic array are also different, which is mainly due to two aspects: first, there are environmental factors in the propagation process of the target sound, resulting in the difference in the time when the target sound reaches each sensor of each basic array; second, due to the geometric relative error in the arrangement of each basic array, the target sound is different. This is also the cause of error. Therefore, the measurement results of the three arrays will be biased. It can be seen from the data table that the error is within 1 m in the three coordinates \(x,y,\) and \(z\). in addition, we use the fusion method to deal with the target position. Different from the simple average of three acoustic basic arrays, it considers the deviation probability of each acoustic basic array, so that the calculated result is closer to the real value. To validate the scientific nature of the algorithm proposed in this paper, practical tests were conducted based on Tables 1-3 and a comparative verification was performed using the dual high-speed camera method for the positioning of UAVs in five instances. The basic principles of the dual high-speed camera method can be referred to in the relevant reference [21]. In the comparative experiments, a synchronous triggering device was used to trigger the target acoustic positioning system of the multiple five-element acoustic arrays and simultaneously trigger the dual high-speed cameras. Image processing recognition was employed in the high-speed cameras to obtain the current spatial position of the UAV. Table 5 presents the positioning results of the method proposed in this paper compared with the dual high-speed camera method. In the table, \(P_{T}(x_{T},y_{T},z_{T})\) represents the results calculated by the high-speed cameras, while \(\Delta P(\Delta x,\Delta y,\Delta z)\) represents the comparative error between the two methods. Compared with the high-speed camera method, the error calculated by the proposed method in this paper ranges from \(-1\) m to 1 m. From the basic principles of the high-speed camera method, it is an ideal test method that is not affected by external factors such as environmental temperature and wind speed, and it possesses relatively high test accuracy. However, for UAV target positioning, the test area range of the high-speed camera method is relatively limited, making it prone to missed captures. In contrast, the method based on multiple acoustic sensor arrays can expand the test range by increasing the number of arrays, making it an important means for future low, slow, and small target positioning tests. However, due to the high sensitivity of acoustic sensors to the environment, there is an inherent uncertain time delay error, leading to relatively lower test accuracy. This paper introduces the VMD method for acoustic information processing and a time calculation method based on correlation functions for multiple acoustic sensors. These methods \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \(\mathbf{P}(\mathbf{x_{A}},\mathbf{y_{A}},\mathbf{z_{A}})\) & \(\mathbf{P}(\mathbf{x_{B}},\mathbf{y_{B}},\mathbf{z_{B}})\) & \(\mathbf{P}(\mathbf{x_{C}},\mathbf{y_{C}},\mathbf{z_{C}})\) & \(\mathbf{P}(\mathbf{x},\mathbf{y},\mathbf{z})\) \\ \hline
1 & 31.54, \(-\)0.25, 14.22 & 32.18, \(-\)0.32, 15.08 & 31.16, \(-\)0.09, 15.30 & 31.64, \(-\)0.23, 14.89 \\
2 & 29.72, \(-\)3.94, 18.26 & 30.88, \(-\)4.21, 18.78 & 30.45, \(-\)3.87, 18.72 & 30.38, \(-\)4.01, 18.61 \\
3 & 31.11, \(-\)0.10, 14.65 & 32.26, \(-\)0.39, 15.89 & 32.98, 0.77, 15.86 & 32.11, \(-\)0.12, 15.48 \\
4 & 30.03, \(-\)1.76, 13.81 & 30.91, \(-\)2.23, 14.56 & 31.45, \(-\)2.35, 14.62 & 30.81, \(-\)2.17, 14.38 \\
5 & 31.19, \(-\)0.86, 13.26 & 32.48, \(-\)1.37, 14.52 & 30.41, \(-\)1.39, 13.98 & 31.34, \(-\)1.23, 13.93 \\ \hline \hline \end{tabular}
\end{table}