DETECTION IMPROVEMENT OF HIDDEN HUMAN’S
RESPIRATORY USING REMOTE MEASUREMENT METHODS
WITH UWB RADAR
Saeid Karamzadeh, Mesut Kartal
Informatics Institute, Department of Advanced Technology, Satellite Communication & Remote Sensing Program Istanbul
Technical Universitym Maslak, 34469, Istanbul, TURKEY
{ Karamzadehsaeid, kartalme}@ itu.edu.tr
Keywords: Gaussian signal, wavelet transforms.
Abstract: Sensing vital parameters especially respiration, is important in many application fields, such as military
services, medical activities, and rescue missions. Because of high resolution and appropriate penetrating
factors, ultra wide band (UWB) radars have got more attention in these applications. In this study, we use
UWB radar to detect respiratory signal of hidden human behind a wall. Acquiring the optimal transmitted
signal, to obtain the best detection result in receiver’s output is the novelty and the aim of our proposed
work. For this purpose, we test Gaussian signal and some derivatives of this signal as transmitted signal and
compare the receiver output results. To extract the required information about the target from receiving
signal and subtract the background noise, the wavelet transforms are the adequate methods and are used in
this work. At the signal processing part, different wavelet transforms will be considered, depending on some
parameters such as the distance between the radar and the target or the wall’s substance. With choosing the
appropriate wavelet transform we can detect accurate human breathing signal affected by background noise
that will be shown in the obtained results.
1 INTRODUCTION
One of the most important challenges about using
UWB signals is to eliminate environmental noises
from the desired signal.
In spite of many advantages, UWB signals are
always exposure to noise because of operated
frequency domain. Also in human respiration
detection with UWB radars, background subtraction
is always considered, because of the sensitivity of
respiration signal and its enormous influence ability
by environmental noises.
Different methods have been used for this
application. Using matched filters in receiver is one
of these methods, which is most often used to obtain
human respiration signal in absence of barriers like a
wall (
Goswami et al., 2012).
The most common application of this method is
taking care of patients and elderly exposure to
cardiac arrest at hospital and at home.
In second method, averaging and Hilbert’s
transform are used in signal processing section to
obtain respiration signal from received signal (
Zhao
et al., 2010
).
Also other methods like using wavelet transform
for extracting respiration signal in signal processing
section are appropriate for complex environments
with barriers like a wall. In this paper, different
wavelets for extracting the optimized detection
results from received signal are analyzed and
compared.
This method can be applied in different fields
like detecting location of criminals hidden inside of
the apartments by police forces, or finding wounded
people buried under debris of disasters like
earthquake or avalanches.
104
Karamzadeh S. and Kartal M.
DETECTION IMPROVEMENT OF HIDDEN HUMANâ
˘
A
´
ZS RESPIRATORY USING REMOTE MEASUREMENT METHODS WITH UWB RADAR.
DOI: 10.5220/0004785901040108
In Proceedings of the Second International Conference on Telecommunications and Remote Sensing (ICTRS 2013), pages 104-108
ISBN: 978-989-8565-57-0
Copyright
c
2013 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2 GENERAL FORM OF SYSYEM
Figure 1, shows the signal changing during the
scattering state that can be expressed as in (Eq. 1).
11
23
1
4
() ( )
() , () ,
()
() ( )
dS t dS t
St St
dt dt
dS t
St ht dt
dt
τ
τ
τ
+
==
+
=×−
∫
(1)
In this figure S
1
(t) is the transmitted signal,
τ
is the
time delay and h is the channel function indicating
the reflection model of the target.
The signal released from transmitter antenna
changes according to the antenna characteristics.
Then it hits different layers of barriers and target
with different dielectric constants and signal changes
accordingly. At last, after time delay and assembling
with channel noise, the signal reaches to receiver.
Choosing an appropriate antenna for better
radiation and also choosing a suitable signal in
transmitter can improve the results of target
detection accuracy. To extract more complete
information about the target, an appropriate signal
processing method can be used.
In the next section, the appropriate antenna types
and the transmitter signal have been discussed.
Finally, obtained results have been presented after
applying the proposed signal processing method. It
is worth noting that in this work, CST microwave
studio (computer simulation technology) software is
used to simulation the antenna-target model and
obtain the received signal
Figure1: Signal changes during forward and backward
path to radar
2.1 Antenna
The most proper antennas used in UWB radars are
Horn and Vivaldi antennas which in most of the
published papers. These antennas have been used as
both the transmitter and the receiver antenna.
Vivaldi antenna gets to use because of its wide
bandwidth and high gain and simple shape. Another
advantage of this antenna is, easy fabrication for
PCB (Printed Circuit Board) products. Horn antenna
also has high gain and because of ability of
decreasing the operating frequency to pass from
barriers with high dielectric constants is more
adequate. In this work, the horn antennas result has
been discussed
Figure 2 shows a man behind a wall with real
dimensions in the presence of horn antenna. Figure 3
shows the used CST human model in simulations
which is a complete model of human presence
considering all organs to obtain real results.
Figure.2: Using horn antenna for human detection
Figure 3: The model of human body that used in
simulation
Detection Improvement of Hidden Human’s Respiratory Using Remote Measurement Methods with UWB Radar
105
2.2 Signals
The most commonly used signal in UWBs is
Gaussian signal and some of its derivatives. The
most important advantage of this signal is its
localization in both time and frequency domains.
In this paper, Gaussian signal, sinusoidal
Gaussian and seven derivative of Gaussian signal is
used as transmitted signal from antenna (transmitter)
and the obtained results from these signals are
analyzed individually in the receiver.
Figure 4 shows Gaussian signal and some of its
derivatives used in this work.
Figure 4: Gaussian signal and some of its derivatives
2.3 Wavelet transform
Wavelet transform can work with non-stationary
signals. And therefore in this paper it is preferred to
the others like Fourier transform.
Wavelet transform also provides multi resolution
analysis with dilated windows which makes it
possible to check different resolutions in various
frequencies. So, wavelet transform would be a good
choice for processing the received signals that
change during passing different layers like air and
wall.
In this paper, different wavelets transforms for
extracting the optimized results from received signal
are used. Some of them are: Daubechies, Symlets,
Coiflets, Meyer, Gaussian, Mexican hat and Morlet
wavelet. For the received signal f(t), its wavelet
transform is given by,
1
(,) () ( )
f
t
Wa
f
th dt
a
a
τ
τ
∗
−
=
∫
(2)
(h is the mother wavelet)
3 RESULTS AND DISCUSSION
In this study, 9 different types of signals containing
Gaussian and sinusoidal Gaussian and seven
derivatives of Gaussian signal are used as
transmitted signal. Figure 5 shows the sample of
these signals. The returned signal from target is
received by the receiver antenna and to extract target
information, proposed signal processing method is
used to analyze. Figure 6 shows some examples of
received signals.
(In all figures the horizontal axis is
time and the vertical axis is amplitude)
Figure 5a: Gaussian signal using as transmitted
signal.
Figure 5b: First derivatives of Gaussian signal using
as transmitted signal
Figure 6a: The received signal when Gaussian signal
used as transmitted signal
106
Figure 6b: The received signal when first derivatives
of Gaussian signal used as transmitted signal
3.1 Signal processing
As explained in the first section, the shape and
bandwidth of transmitted signal changes during
passing the wall, hitting the target and returning to
the receiver antenna. Wall thickness and its
substance and distance between the human and
antenna also affect the received signal.
By using appropriate signal in transmitter, the
received signal can be predicted. As derivative of
transmitted signal, the bandwidth of the signal
changes and appears in receiver. Using wavelet
transform, because of predicting the occurred
changes like waveform and received signal
bandwidth, is helpful in background subtraction and
results appropriate output signal. Figure 7 shows
diagrams of final data resulted from signal
processing part which is human respiration periodic
signal.
The obtained results show that, Mayer and
Morlet wavelets give the best results by sending first
derivative of Gaussian signal in transmitter. Also
between these two wavelets, Mayer is closer to
received signal and has better results. In these
results, the background noise is completely omitted
and respiration signal with acceptable amplitude can
be observed (Figure 7a).
By sending the second derivative of Gaussian
signal, Mexican hat wavelet gives better and
acceptable output. By the way, Mayer wavelet
would be more suitable as the transmitted signal in
sending the second derivative of Gaussian signal
(Figure 7.b).
During simulation process, it could be
understood that the forth derivative of Gaussian
signal in transmitter would have the best results
using Morlet. For other derivatives (5, 6, 7), using
Coiflets, Symlets and Daubechies wavelets would
have better results which among them. Symlets
would be more appropriate for fifth derivative and
Daubechies would be more appropriate for sixth
derivative of Gaussian signal.
It can be concluded from the results that the
transmitted signal is formed by the transmitting
antenna and then it can be interpreted as the second
derivative of the transmitted signal at the receiver.
Considering this, the best results for the target can
be obtained by choosing appropriate wavelet.
In forward, after different simulations, it could
be observed that second derivative of Gaussian
signal would be the best choice for similar
environments. Also, if the distance between human
and antenna increase, using Daubechies wavelet
would be a better choice for extracting desired target
specifications. About walls with higher dielectric
constant, using second derivative of Gaussian signal
with Morlet wavelet would give the best results for
target specifications.
Figure 7a: the result of Meyer wavelet when first
derivatives of Gaussian signal used as transmitted signal
Figure 7b: the result of Mexican hat wavelet when
second derivatives of Gaussian signal used as transmitted
signal
(Vertical axis show the amplitude of breathing signal and
the horizontal axis show the repeat of this signal)
Detection Improvement of Hidden Human’s Respiratory Using Remote Measurement Methods with UWB Radar
107
Figure 7c: one pulse of received signal after using
wavelet methods
4 CONCLUSION
In this paper, human respiration detecting is
considered. At first, the general UWB system used
in detection is discussed. Then the choice of the
antennas used in this work is presented. According
to the importance of the transmitted signal form,
appropriate transmitting signal for improving
detection and obtaining better results are introduced.
Finally, the wavelet transform method as the best
method for background subtraction is used and
simulation results are presented. It is concluded that
for different system geometries, appropriate
derivative of the transmitted signal and the mother
wavelet selection give the best result. Additionally
the respiratory signal of more than one hidden
humans and moving humans could be consider in
next work.
ACKNOWLEDGMENT
I would like to thank CST team for providing me CST
software and voxel data for simulation part of this work.
REFERENCES
Goswami, D., Borkotoky, S. S., Mahanta, A., Sarma, K.
C., 2012. A Matched Filtering Technique for
Noninvasive Monitoring of Human Respiration Using
IR-UWB Radar, International Journal of Advanced
Technology & Engineering Research (IJATER), 2 (4),
2250-3536
Zhao, X., Gaugue, A., Liebe, C., Khamlichi, J., Menard,
M., 2010. Through the Wall Detection and
Localization of a Moving Target with a Bistatic UWB
Radar System, Proceedings of the 7th European Radar
Conference, 978-2-87487-019-4, Paris, France
Immoreev, I., Tao, T.H., 2008. UWB Radar for Patient
Monitoring, IEEE A&E Systems Magazine
Staderini, M.E., 2002. UWB Radars in Medicine,
University of Rome ‘‘Tor Vergata”, IEEE AESS
Systems Magazine
Immoreev, Y.I., Samkov, S., Tao, T.H., 2005. Short -
Distance Ultrawideband Radars, IEEE A&E Systems
Magazine
Poularikas, A.D. (Editor-in-Chief), Sheng, Y., 2010.
Transforms and Applications Handbook, Ch. 10
Wavelet Transforms, CRS Press, 3
rd
Edition
http://www.cst.com/
108