## 6 Test and Analysis  
To verify the algorithm proposed in this paper, we used a low-altitude flying UAV as a target to verify the algorithm. According to the geometric relationship of the acoustic array arrangement in Figures 1 and 2, the point \(O\) is the central coordinate of the system, denoted as \(Oxyz\). Points A, B, and C represent three individual acoustic unit arrays, with each unit array comprising five acoustic sensors. Each unit array has its own central coordinates,where the center origin coordinates of arrays A, B, and C in the \(Oxyz\) coordinate system are (30,0,0), (\(-\)30,0,0), and (0,30,0), respectively. Here, the unit of coordinate parameters is meters. The acoustic sensors in the three unit arrays are of the same model, characterized by high sensitivity and low noise. In this experiment, we selected the AWA14411 model from the Aihua Instrument Company for acoustic sensors to capture explosive sound signals. Each unit array is equipped with a set of PCI-9812 data acquisition cards and an IPC-610-L microcomputer. The data acquisition cards are responsible for collecting target signals from the five acoustic sensors in each unit array and converting them into digital signals for computer processing. The computer handles data storage and, subsequently, the stored signals from the five acoustic sensors are transmitted to the terminal processing subsystem via a wireless transmission module. This subsystem processes and fuses the signals received from the data acquisition cards, ultimately determining the location of the explosive point. In Figure 1, the central coordinates of the three unit acoustic arrays A, B, and C are denoted as \(O_{1}\), \(O_{2}\), and \(O_{3}\), respectively. The relative coordinate positions of the acoustic sensors within each unit acoustic array are \(S_{0}^{A}(0,0,2.4)\), \(S_{1}^{A}(3,0,0)\), \(S_{2}^{A}(0,3,0)\), \(S_{3}^{A}(-3,0,0)\), \(S_{4}^{A}(0,-3,0)\); \(S_{0}^{B}(0,0,2.4)\), \(S_{1}^{B}(3,0,0)\), \(S_{2}^{B}(0,3,0)\), \(S_{3}^{B}(-3,0,0)\), \(S_{4}^{B}(0,-3,0)\); \(S_{0}^{C}(0,0,2.4)\), \(S_{1}^{C}(3,0,0)\), \(S_{2}^{C}(0,3,0)\), \(S_{3}^{C}(-3,0,0)\), \(S_{4}^{C}(0,-3,0)\). To calculate the specific spatial position of the (UAV) target, in this experiment, we collected signals from all sensors within each unit acoustic array, as illustrated in Figures 4-6. In Figure 4, \(S_{0}^{A}-S_{4}^{A}\) represent the signal collected by \(S_{0}-S_{4}\) of the acoustic array A. In Figure 5, \(S_{0}^{B}-S_{4}^{B}\) represent the signal collected by \(S_{0}-S_{4}\) of the acoustic array B. In Figure 6, \(S_{0}^{Q}-S_{4}^{Q}\) represent the signal collected by \(S_{0}-S_{4}\) of the acoustic array C.  
Figure 4: Five acoustic signals in basic array A collected from UAV at a certain moment. By analyzing the signals collected in Figures 4-6, it can be observed that the output signals of the acoustic sensors contain various frequency components, mainly including target signals and background noise signals. The noise manifests as multiple random frequency signals, exhibiting a random Gaussian distribution. It is evident that when a certain frequency of noise signal undergoes a sudden change, it may exhibit characteristics similar to false target signals. When such signals occur at multiple frequencies, it can pose challenges to the recognition of UAV target signals. To eliminate false target signals and interference from other frequency components, we employed the VMD algorithm proposed in the third part of the paper for effective  
Figure 5: Five acoustic signals in basic array B collected from UAV at a certain moment. Figure 6: Five acoustic signals in basic array C collected from UAV at a certain moment. From Figures 7-9, it is evident that in this experiment, due to the relatively short distance of each acoustic array, each of the five acoustic sensors in each array could capture the acoustic information of the UAV target as it traverses the test area.