# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 4 Experimental Results and Analysis
### Comparative Experiment  
Currently, there are relatively few explorations in the field of utilizing neural networks to automatically extract footstep vibration features for localization. To comprehensively evaluate the potential advantages of deep learning techniques in this domain, the present study selected the classical WT-TDOA algorithm as the benchmark comparison algorithm and simultaneously introduced the SAE-BPNN localization method for a comparative analysis of overall performance. Through this comprehensive comparison, the study aimed to further illustrate the significant performance advantages of the proposed localization method and provide valuable references for research in this field. Additionally, to assess the method's robustness, the maximum, minimum, and median errors between the actual position and estimated position along the path were calculated.  
In this experiment, 200 sets of path data were randomly selected from the test set, and the localization errors of different algorithms were analyzed in detail. As shown in Figure 12, from the perspective of positioning accuracy, the WT-TDOA algorithm has the largest error, reaching 0.62 m. This is mainly attributed to the limitations of traditional algorithms in feature extraction, which makes it difficult to fully capture the key information in vibration signals. In contrast, although the SAE-BPNN has improved accuracy and stability, reaching 0.494 m, because of the difficulty of static models in extracting the temporal features of vibration signals, the overall positioning performance is lower than that of our method. Compared to the two algorithms, the positioning error of ours is only 0.376 m. Our method not only shows improvements in the average positioning accuracy but also better reflects the pedestrian movement trend by outputting continuous trajectories through the regression method, showing this method's unique advantages in practical applications.  
Furthermore, the positioning of moving targets in practical applications is often restricted by the requirement of real-time performance. Therefore, in this experiment, the average calculation time for each path predicted by the three methods was further statistically analyzed, as shown in Table 4. Among them, the WT-TDOA algorithm took the longest time, mainly due to the fact that its data preprocessing stage involved a large number of wavelet decomposition calculations, significantly increasing the complexity and time cost of the calculation. In contrast, the average calculation time of our method was the shortest at 16.58 s, which was mainly attributed to its efficient feature extraction and temporal learning capabilities.  
In conclusion, compared to traditional algorithms, WT-TDOA and SAE-BPNN, our method not only demonstrated certain advantages in localization accuracy and stability but also required less computation time, making it more suitable for meeting real-time requirements in practical applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ATE (m)** & **RTE** & **Multiply–Addition Degree** \\ \hline ResNet18 & 0.761 & 3.3\% & 565.407 M \\ ResNet50 & 0.623 & 2.4\% & 1127.54 M \\ Cam + ResNet18 & 0.610 & 2.5\% & 565.408 M \\ ResNet18 + Bi-LSTM & 0.539 & 2.2\% & 615.044 M \\ Our method & 0.441 & 1.3\% & 615.044 M \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimenter information sheet.