## 5 Results
### Locating Results with Filtering  
In order to analyze the influence of the filter bands on the locating results, the impact signals filtered by different frequency bands were used for locating. After the wave velocity measurement signals of Section 3.1 were filtered by different frequency bands, the signal wave velocity of this frequency band was calculated.  
The distributions of absolute errors using the signals with 0-100 kHz, 100-200 kHz and 200-300 kHz filtering respectively are shown in Figure 15.  
It can be seen from Figure 15 that the locating results after 0-100 kHz filtering fluctuated greatly. Errors of five points were greater than 50 mm, errors of 23 points were less than 30 mm, and the locating stability was not high. For 200-300 kHz filtering, there were three points with errors greater than 50 mm and 27 points with errors less than 30 mm, similar to the unfiltered results. For 100-200 kHz filtering, the maximum error of 32 impact points locating results was only 16.3 mm, the average absolute error of 96 groups of data was 7.0 mm, the average relative error was 0.86%, the error standard deviation was 3.5 mm, the locating accuracy was high and the stability was good, which verifies the correctness of the simulation.  
For the impact points near the center of the sensor triangle, various methods can obtain better locating results. However, it was relatively difficult to locate the impact points near the triangle boundary, especially the points near the vertex. In order to further analyze the cause of the locating  
Figure 14: The distribution of absolute errors using the signals without filtering.  
Figure 15: The distributions of absolute errors using the signals with different frequency bands filtering: (a) 0–100 kHz; (b) 100–200 kHz; (c) 200–300 kHz.  
error, the time domain waveform of a group of experimental signals in the impact experiment at point D1 (in Figure 12) was taken as the research object, and the arrival time of the signal calculated by each method was compared with the actual arrival time.  
The comparison of the calculated arrival time and the actual arrival time of the signals with different frequency bands filtering is shown in Figure 16. It can be seen from the figure that the error was mainly caused by two reasons: (1) the noise was complicated before the S0 mode arrived, and the threshold intercepted the noise signal; (2) The threshold was so large that it skipped the head of S0 mode.  
This is mainly because the situations of passing through the stiffeners greatly differed in which impact signal at the point D1 propagated to sensors, which made the signals received by the sensors greatly different in the time domain and the frequency domain. The S0 mode filtered in the 100-200 kHz band had the relatively clearer head, which was easier to distinguish from noise. The threshold obtained  
Figure 16: The comparison of the calculated arrival time and the actual arrival time of the signals with different frequency bands filtering: (**a**) signals of sensors 1–3 without filtering; (**b**) signals of sensors 1–3 with 0–100 kHz filtering; (**c**) signals of sensors 1–3 with 100–200 kHz filtering; (**d**) signals of sensors 1–3 with 200–300 kHz filtering.  
by the AECTF method based on the energy compensation can truly reflect the signal characteristics, and adaptively intercepts the arrival time of the S0 mode, thereby achieving correct locating.