# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 5 Conclusions  
Addressing the issues of limited accuracy, uniform feature extraction methods, cumbersome parameter adjustments, and the inability to output continuous dynamic trajectories in existing indoor positioning methods based on vibration signals, this paper proposes a deep learning-based approach for locating moving targets using vibration signals. This method involves the construction of a neural network model aimed at establishing a mapping relationship between footstep vibration signals and pedestrian speed. To validate the effectiveness and accuracy of the proposed method, ablation experiments and comparative experiments were designed. The experimental results demonstrate that the network model, which incorporates the multivariate time-series characteristics of footstep vibration signals, achieves high precision. On the FVS dataset, the proposed method significantly improves positioning accuracy, reducing the average system positioning error to 0.376 m. Compared to WT-TDOA and SAE-BPNN, the positioning accuracy is enhanced by 37.9 % and 24.8%, respectively.  
However, we acknowledge the inherent limitations of the proposed method when dealing with complex scenarios, particularly in multi-person walking situations where the signals captured by the vibration sensor network are confounded by footstep vibration information from different individuals. The current study is not yet capable of accurately identifying the specific number of people within the active area, nor can it effectively distinguish these mixed vibration signals, thereby constraining the widespread application of vibration signal localization technology.  
In light of this, our team's future research will concentrate on leveraging the unique differences in footstep characteristics among individuals and integrating deep learning  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Mean Computation Time (s)** \\ \hline WT-TDOA & 27.31 \\ SAE-BPNN & 20.56 \\ Our method & 16.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimenter information sheet.  
Figure 12: Comparison of positioning results of different methods.  
techniques to extract crucial features of footstep variability, with the objective of achieving precise estimation of the number of vibration sources and effective separation of the source signals.  
Conceptualization, R.C.; methodology, R.C. and Y.Z.; software, R.C.; validation, R.C., Y.Z. and Q.C.; formal analysis, Y.Z.; investigation, R.C., Y.Z. and C.Z.; resources, Y.Z.; data curation, R.C.; writing--original draft preparation, R.C.; writing--review and editing, R.C.; visualization, C.Z.; supervision, Y.Z.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Jiangsu Graduate Research Fund Innovation Project] grant number [KYCX23_3059] and the APC was funded by [Changzhou University].  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to considerations of privacy protection and ethical principles.  
The authors declare no conflict of interest.