Aerospace and Electronic Systems Magazine May 2018 - 18

WiFi-Based Through-the-Wall Presence Detection
ered the following classes: empty room,
presence (static), and presence (dynamic). The results for the three setups
are presented in terms of confusion matrices in Tables 2-4, reporting both the
correct estimation percentage and misclassification percentage for each class.
The achieved average accuracy is rather
good and ranges from 95%, obtained in
the double TTW scenario (the most challenging), to 91% and 99%, obtained in
the case of a single TTW configuration.
These results confirm the effectiveness
of the proposed solution.

CONCLUSIONS

Figure 4.

Scatter plot of the selected features, i.e., spectral spread and Doppler ratio, as achieved in Setup 1.

Figure 5.

Scatter plot of the selected features, i.e., spectral spread and Doppler ratio, as achieved in Setup 2.

This article proposes a WiFi-based device-free TTW human detection system
on the basis of the use of CSI extracted
from commodity hardware. Experimental results show that the proposed
method, on the basis of the mean Doppler spectrum, is very effective in distinguishing the three classes: i) empty;
ii) a stationary person; and iii) a moving person. Particularly interesting are
the results on the detection of stationary
humans, which is rather challenging and
also not in TTW scenarios. The achieved
average accuracies are 99% and 91% in
a single TTW configuration in two different rooms, while it is 95% in a double
TTW configuration, i.e., the activities
are performed in a room different from
those ones in which the WiFi transmitter and receiver are placed. The proposed
method has also a very low complexity,
requiring only FFT operations, computation of just two features, and classification by using a simple naïve Bayes classifier.

ACKNOWLEDGMENT
The publication was prepared with the
partial support of the "RUDN University
Program 5-100′'.

REFERENCES
[1]

Figure 6.

Scatter plot of the selected features, i.e., spectral spread and Doppler ratio, as achieved in Setup 3.

18

IEEE A&E SYSTEMS MAGAZINE

Li, J., Zeng, Z., Sun, J., and Liu, F.
Through-wall detection of human being's
movement by UWB radar. IEEE Geoscience Remote Sensing Letters, Vol. 9, 6
(2012), 1079-1083.

MAY - JUNE 2018



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