# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method
### Vibration Signal Preprocessing Module  
Floor vibration signals, induced by footsteps, typically encompass frequency components below 100 Hz. A geophone is inherently a second-order high-pass filter with a  
Figure 1: Location scenario. This figure shows the experiment scene of vibration signal acquisition of a moving target, including the sensor configuration, single moving target, and one camera.  
Figure 3: Architectures of our method.  
Figure 2: Overview of our method. This paper proposes a deep learning-based method for locating moving targets using vibration signals. The approach begins by collecting vibration signal data sequences and recording the actual trajectories of pedestrians on the ground using cameras. Subsequently, supervised learning is employed, leveraging deep learning techniques to extract signal features for training the positioning model. Finally, the two-dimensional velocity vectors of pedestrians are output to estimate their motion trajectories.  
high-frequency cutoff below 10 Hz (implying that as the frequency decreases from 10 Hz to 1 Hz, the geophone's response may attenuate to one percent of its original value). Artifacts within the signal primarily originate from environmental high-frequency noise exceeding 100 Hz. To mitigate its impact, a second-order Butterworth low-pass filter with a cutoff frequency of 100 Hz [25] is first used to filter the signal. Then, normalization is performed to unify the dimensions of the signal. The normalized vibration signal is denoted as \(\mathbf{a}(t)\):  
\[\mathbf{a}(t)=\{a_{1}(t),\dots,a_{i}(t),\dots,a_{M}(t)\} \tag{1}\]  
where \(a_{i}(t)(-1\leq a_{i}(t)\leq 1)(1\leq i\leq M)\) represents the result of \(e_{i}(t)\) after low-pass filtering and normalization.  
Considering that the velocity of individuals is not constant during walking, a sliding window approach [26] is adopted to partition the normalized vibration signal \(a(t)\) in order to more accurately capture velocity changes within each time window. The specific operations are as follows: starting from the beginning of the data stream at \(t=0\) seconds, a window length of \(L=1\) second (based on the consideration of a normal human gait cycle) is set, and the number of samples contained in each window, \(N_{s}=f_{s}\times L\), is determined based on the sampling rate \(f_{s}\). Then, the window is slid sequentially according to the set moving step size \(d\) to segment the entire signal. If the total length of the signal is \(N\), a signal matrix of \(k\) windows each of length \(N_{s}\) is obtained. When the window moves to the \(j\)th sample point, the \(r_{th}\) signal matrix is represented as \(\mathbf{U}_{j}^{r}\in R^{M\times N_{s}}\):  
\[k=\left[\frac{N-N_{s}}{d}\right]+1 \tag{2}\]  
\[\mathbf{U}_{j}^{r}=\left(\begin{array}{cccc}u_{1,j-N_{s}}&u_{1,j-N_{s}+1}& \cdots&u_{1,j}\\ u_{2,j-N_{s}}&u_{2,j-N_{s}+1}&\cdots&u_{2,j}\\ \vdots&\vdots&\vdots&\vdots\\ u_{i,j-N_{s}}&u_{i,j-N_{s}+1}&\cdots&u_{i,j}\\ \vdots&\vdots&\vdots&\vdots\\ u_{M,j-N_{s}}&u_{M,s,j-N_{s}+1}&\cdots&u_{M,j}\\ \end{array}\right) \tag{3}\]  
where \(i\) represents a particular channel signal.