## 6 Experiments and Results Analysis  
### Feature Extraction  
The number of test subjects is 10. The walking paths are A0(1,0) to A1(1,6), B0(2,0) to B1(2,6), C0(3,0) to C1(3,6), D0(4,0) to D1(4,6), E0(5,0) to E1(5,6), and F0(6,0) to F1(6,6). The test subjects were required to walk 10 times along each line, resulting in a total of 600 samples. Before feature extraction, the original data needs to be preprocessed, which includes waveform interception and noise cancelling. This paper adopts the method of noise cancelling. The sample is intercepted 300 points from the first peak of the waveform. The intercepted time-domain waveform of the test subject A and B are shown in Figure 9. In this figure, the abscissa represents time (s) and the ordinate represents the Voltage (V) value. And, each column represents different signal from 4 sensors (Row) for the same path. Each row represents different signal from the same sensor for 6 paths (Column), which can be seen in Figure 7.  
On one hand, the time-domain waveform of single target shows that there are some differences between the signal waveform from the same sensor node when the target walking in different paths. This is because of the changes in the relative position of the human target. With the increase of the distance, pyroelectric infrared sensor can get less and less human pyroelectric infrared signal which results in the decrease of amplitude. On the other hand, for the same distance target, there are significant differences  
Figure 9: The figure is the time domain waveform for target A and target B, where the blue solid line represents the target A; the black dotted line represents the target B; the abscissa represents time; the ordinate represents voltage; each column represents four sensors in different heights; each row represents six kinds of distances.  
in the observed signal waveform from different PIR nodes. The reason is that the PIR sensor module locates at different heights would detect the signal from different parts of human target body, which leads to the differences in the final waveform.  
In the scenario of the same distance and PIR node, there is obvious difference between the observed signals from different targets caused by different characteristics of different targets.  
As for the time domain signal, the four algorithms have been introduced in Section 5, which are used for feature extraction. The result of feature extraction of the two targets waveform is shown in Figure 10.  
Figure 10a is feature extraction of FFT algorithm, the abscissa represents the frequency; the ordinate represents the amplitude; the blue solid line is spectrum feature of target A; the red dotted line is spectrum feature of target B.  
Figure 10b is feature extraction of STFT algorithm, the red line indicates that singular value distribution of time-frequency matrix of target A; the green line indicates that singular value distribution of time-frequency matrix of target B.  
Figure 10c is feature extraction of WT algorithm, the blue solid line indicates that WT energy distribution of target A; the red dotted line indicates that WT energy distribution of target B.  
Figure 10d is feature extraction of WPT algorithm; the blue solid line indicates that WPT energy distribution of target A, the red dotted line indicates that WPT energy distribution of target B.  
Figure 10: Feature extraction by different algorithms. (**a**) Feature extraction by FFT. (**b**) Feature extraction by STFT. (**c**) Feature extraction by WT. (**d**) Feature extraction by WPT.