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DOI. No. 10.1109/MAES.2018.170124

WiFi-Based Through-the-Wall Presence Detection of Stationary and Moving Humans Analyzing the Doppler Spectrum Simone Di Domenico, Mauro De Sanctis, Ernestina Cianca, Marina Ruggieri University of Rome Tor Vergata, Rome, Italy

INTRODUCTION Through-the-wall (TTW) sensing is relevant in several scenarios.

In particular, a system able to detect the presence of a noncollaborative person behind a wall could be used by law enforcement for better planning actions in case of standoffs and hostage situations. For emergency situations, first responders could use such a system to detect the presence of people through rubble and collapsed structures. Traditionally, these types of systems have been designed using a radar approach. In particular, ultrawideband systems (2 GHz of bandwidth) have been proposed for detecting human presence by using radio frequency (RF) signals [1], [2]. These systems usually require a large power source and big antennas. Recently, to reduce the power and complexity of these devices, the use of opportunity signals, such as WiFi signals, has been considered [3]–[6]. However, most of the mentioned approaches are still radar-like. For instance, in [4], they capture the WiFi signals reflected by the body of a person moving behind the wall, and by using inverse synthetic aperture radar processing, they are able to track the person as he or she moves behind the wall. Moreover, in this work, the WiFi transmitter is located close to the wall, and it is not just an access point (AP) of opportunity.

A different device-free presence detection approach using WiFi signals is based on the fact that the presence or activity of a human being inside a room changes the propagation channel of the RF signal and, in particular, the multipath characteristics. Therefore, by studying how the channel varies over time, presence or activity may be recognized [7], [8]. In most of these works, it is explicitly mentioned that one advantage in using such RF signals is that they

enable also presence detection or activity recognition (AR) TTW. Nevertheless, there are not many works on TTW presence detection and AR on the basis of WiFi signals and, more specifically, on the use of channel state information (CSI). TTW RF sensing is challenging for two main reasons: 1. The signal-to-noise ratio is lower.

2. The signal paths reflected by the human body are more unlikely to reach the receiver.

One work on the use of CSI from commodity WiFi devices for TTW detection is presented in [3]. However, the proposed system is only able to detect a person walking, even if slowly, in the room behind the wall. However, the automatic detection of people in stationary positions, i.e., sitting or standing firm, is also important.

In this article, we present a TTW presence detection system for both stationary and moving persons. The proposed system uses the WiFi signal transmitted by a single WiFi AP. The assumption is that in the case of an empty environment, with one stationary person or a moving person in the room, the channel frequency response changes over time in different ways. To understand how the channel frequency response varies over time, the mean Doppler spectrum computed on the extracted CSI is used. The presence detection is then performed through a classification process applied to a selected set of features calculated on the mean Doppler spectrum. Through experimental results, this article shows the feasibility and effectiveness of the proposed approach for the detection of stationary humans, which is usually rather challenging in TTW and non-TTW scenarios.

RELATED WORKS Authors' current address: S. Di Domenico, M. De Sanctis, E. Cianca, M. Ruggieri, Department of Electronics Engineering, University of Rome, Tor Vergata, 00133 Rome, Italy, E-mail: (mauro.de.sanctis@uniroma2.it). M. De Sanctis is also with the Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation.

Manuscript received June 15, 2017, revised October 31, 2017, and ready for publication December 19, 2017.

Review handled by L. Ligthart.

0885/8985/18/$26.00 © 2018 IEEE 14

The first example of opportunistically using WiFi signals for human detection was presented in [9]. A TTW human detection system passively using WiFi signals was designed and implemented in [5]. However, it can only detect line-of-sight people crossing between the transmitter and receiver.

In [6], the feasibility of detecting people moving behind walls by using passive bistatic WiFi radar at standoff distances is investigated. The experimental data were acquired by using University College London's multistatic netted radar system that consists of

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three synchronized networked nodes and has an operational frequency of 2.4 GHz. The three nodes were configured in a receiveonly mode, while a commercial AP was the transmitter of opportunity. The master node (Node 1) distributes a 100-MHz clock signal to Slave Nodes 2 and 3.

Authors of [10] presented RF capture, a system that captures the human figure, i.e., a coarse skeleton, through a wall. RF capture uses a combination of a two-dimensional antenna array and frequency-modulated continuous wave radar chirps to scan the surrounding three-dimensional space for RF reflections.

In [11], the authors implemented a simple radar method to determine Doppler frequency shift of a moving person. They transmitted a 1-kHz tone by using a 5.5-GHz carrier frequency and sampling the received signal at a rate of 200 ksamples/s.

The Wi-Vi system is presented in [4] to track people behind walls. Wi-Vi is essentially a three-antenna multiple-input and multiple-output (MIMO) device using 20-MHz WiFi orthogonal frequency division multiplexing signals. The system consists of three Universal Software Radio Peripheral (USRP) platforms connected to an external clock so that they act as one MIMO system.

Two of the USRPs are used for transmitting and one for receiving.

It also uses directional antennas to focus the energy toward the wall or room of interest. MIMO nulling is implemented directly into the USRP hardware driver so that it is performed in real time.

Postprocessing is performed offline in MATLAB by using the smoothed MUSIC algorithm to compute the power received along a particular direction. The Wi-Vi approach requires full control of both transmitter and receiver; hence, it currently cannot be applied to commercial WiFi devices.

In [3], a TTW human detection system using CSI from commodity WiFi devices is presented. They apply a principal component analysis-based filtering to clean the collected CSI, and then they exploit the correlated changes over different subcarriers and propose a subcarrier dimension-based feature, i.e., the mean of the first-order difference of eigenvectors. Experiments have been conducted in two different indoor environments, achieving both a true-positive rate and a true-negative rate of up to 99%. However, the experiments have been carried out with the strong assumption that volunteers walk with a slow or normal speed. No stationary humans are considered.

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EXPERIMENTAL SETUP AND DOPPLER SPECTRUM ESTIMATION Experiments have been carried out in three different setups involving three adjacent environments, i.e., Setup 1, Setup 2, and Setup 3, in Room A, Room B, and Room C, as shown in Figure 1. Each experiment has been performed in a TTW configuration. Particularly in Setup 3, the double TTW scenario has been tested, i.e., the transmitter and receiver are placed in different rooms from the one in which the human activities are performed. During the experiments, the following conditions have been considered: 1. Empty: the considered room is empty during the experiment.

2. Presence (static): one person is inside the considered room in a sitting or standing fixed condition and in different positions.

3. Presence (dynamic): one person is inside the considered room, walking over different patterns.

The considered rooms have different sizes and are separated by 12 cm of drywall. In each setup, the transmitter AP is located on the opposite side of the wall of interest.

The proposed system includes a commercial 2.4-GHz WiFi AP with three antennas acting as transmitter and a laptop having a WiFi card Intel 5300 with three antennas acting as receiver. The laptop, running Ubuntu 10.04 LTS, operates by sending Internet Control Message Protocol (ICMP) echo request packets every Tp = 10 ms to the AP and waits for an ICMP echo reply from the AP. CSIs are extracted from the received ICMP echo reply packets by using a customized firmware and an open-source Linux wireless driver for the Intel 5300 WiFi card [12], [13]. The AP uses a double-stream transmission; hence, a total of Nch = 6 CSI estimac c tions H m = H m (l ) of length Nsub = 30, l = 1, 2, ..., 30, are collected for each ICMP echo reply packet received at time m. Each element of a CSI vector H cm represents the complex channel gain for a particular subcarrier, given the channel index c and the time index m.

Then, a unique CSI is obtained by concatenating Nch CSI vectors, resulting in a global CSI vector H = Hm(l) of length Nel = 180, l = 1, 2, ..., Nel. To remove the power fluctuations of the AP, each global CSI vector is normalized by its mean value.

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Figure 1.

Experimental setup.

Figures 2 and 3 show the mean Doppler spectrum computed Our recognition method is based on the analysis of the over the three different classes, for Setup 1 and Setup 2, respecmean Doppler spectrum that is computed as follows. Consider tively. As expected, in case of the presence of a person, either the time-varying channel transfer function H(m, l) = Hm(l) that in a static or dynamic condition, a larger spread with respect to represents the magnitude of the global CSI at time m for each empty room is observed. Moreover, Figures 2 and 3 show that subcarrier l. Then, the output Doppler spread function G(k, l), or bifrequency function, is found by computing the discrete Fourier transform (DFT) of H(m, l), with respect to the time variable m [14]. The DFT is computed over a sliding time window of W = 10 s, which means that for every new received CSI vector, a new G(k, l) is calculated. The size of W has been chosen as a trade-off between the accuracy in the classification task and the recognition delay, i.e., the time interval after which a new estimation of the human presence is completely updated. Note that the output Doppler spread function G(k, l) represents the Doppler spectrum associated with each subcarrier l. Finally, we obtain the mean Doppler spectrum D(k) by Figure 2.

Magnitude of average Doppler spectrum for empty room, presence (static), and presence (dynamic) taking the average of G(k, l) over the scenarios obtained in Setup 1. subcarrier index l.

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Di Domenico et al. a moving person in the environment causes a higher spreading of Doppler spectrum than that of a static person. Starting from that result, which confirms the intuition behind our key idea, it seems feasible to find some features that quantify the spreading of the Doppler spectrum and allow recognizing different scenarios.

FEATURE EXTRACTION FROM THE DOPPLER SPECTRUM The mean Doppler spectrum D(k) provides the mean contribution of all the subcarriers to the Doppler frequency k. The Doppler spectrum is a measure of the weighted distribution of the speed of the scatterers. Because most of the scatterers are usually fixed, the mean Doppler spectrum D(k) is concentrated on the 0-Hz Doppler frequency. The presence detection of a person in a static or dynamic condition is achieved through a classification task applied to a selected set of spectral features of the mean Doppler spectrum. A nonexhaustive list of spectral features that can be computed on the mean Doppler spectrum is shown in Table 1. See [15] for a more detailed description of the features.

FEATURE SELECTION AND CLASSIFICATION In this work, we applied a naïve Bayes classifier to a set of features computed on the mean Doppler spectrum. The data set containing the overall set of features has been divided into a training set (50%) and a test set (50%).

Starting from the list of features provided in Table 1, we have computed the mean classification accuracy over the test environments for each pair of features. Then, we have chosen the pair of features that provides the best classification accuracy over both environments. As a result, the selected pair of features are spectral spread and Doppler ratio of the mean Doppler spectrum. The scatter plots for the selected features are shown in Figures 4–6 for Setup 1, Setup 2, and Setup 3, respectively.

Table 1.

List of Features Extracted from the Mean Doppler Spectrum Feature

Definition

Root mean square Doppler bandwidth

Is the square root of the second moment of the Doppler spectrum

Spectral spread

Is the second central moment of the Doppler spectrum with log frequencies

Spectral roll-off

Is the 95th percentile of the Doppler spectrum distribution across frequencies

Spectral slope

Is the rate of falloff of the Doppler spectrum

Spectral flatness

Is a measure of the flatness of the Doppler spectrum

Spectral skewness

Is a measure of the asymmetry of the Doppler spectrum about its mean

Spectral kurtosis

Is a measure of the tailedness of the Doppler spectrum

Doppler ratio

Is the ratio between the amplitude of the third and the first component the Doppler spectrum Using these features, we note from the scatter plots that different classes are grouped in various clusters in the feature space, and this allows us to achieve very good performance during the classification task, as will be shown in the next section.

EXPERIMENTAL RESULTS

Figure 3.

Magnitude of average Doppler spectrum for empty room, presence (static), and presence (dynamic) scenarios obtained in Setup 2.

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As explained in the previous section, experiments have been carried out in different positions of a person, in different static conditions (sitting or standing firm), and in three different setups. The classifier has been trained separately for each position, using 50% of each data set. We have consid-

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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

Figure 6.

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

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[1] 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.

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Di Domenico et al.

Table 2.

Confusion Matrix for Setup 1 Prediction Truth

Empty Room

Presence (Static)

Presence (Dynamic)

Empty room

0.97

0.03

0

Presence (static)

0.01

0.99

0

Presence (dynamic)

0

0

1

Table 3.

Confusion Matrix for Setup 2 Prediction Truth

Empty Room

Presence (Static)

Presence (Dynamic)

Empty room

0.75

0.25

0

Presence (static)

0

0.98

0.02

Presence (dynamic)

0

0

1

Table 4.

Confusion Matrix for Setup 3 Prediction Truth

Empty Room

Presence (Static)

Presence (Dynamic)

Empty room

0.90

0.10

0

Presence (static)

0.05

0.95

0

Presence (dynamic)

0

0

1

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[2] Liang, S. D. Sense-through-wall human detection based on UWB radar sensors. Signal Processing, Vol. 126 (2016), 117–124.

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