IEEE Circuits and Systems Magazine - Q1 2021 - 50

analysis to detect vital signs in different scenarios using
a CW Doppler radar [34]. Besides that, the SNR of the
reflected signals from human subjects can be improved
for a CW radar, as demonstrated in [35]. Classification
algorithms were also used to identify six different human
subjects in this work, and therefore can be used to detect
vital sign fingerprints. Frequencies generally used in CW
radars for detection purposes starts from as low as 2 GHz
[32] up to 110 GHz [25], whereas other commonly used
bands are centered at 2.4 GHz, 5.8 GHz, 10 GHz, 24 GHz,
and 60 GHz, as summarized in Table 5. Higher sensitivity
to chest displacements is exhibited in CW radars with
increasing frequency (towards millimeter-waves) due to
the shorter wavelength. On the other hand, millimeterwaves frequency operation attenuates more easily, especially when the signal is transmitted through highly
lossy human tissues such as muscles or skin [59].
B. UWB Impulse Radar
UWB impulse radar operates throughout a wide bandwidth, and consistently transmits narrow pulses (or
impulses) to the target. Then, information about the
target range can be extracted with high resolution by
processing the received echoes. It is also capable of target localization, tiny motion detection and through wall
detection. On the other hand, due to the moderately
high noise accompanying the signal, UWB-IR typically
has low SNR. Moreover, it is more complex in architecture, and thus more costly to implement compared to
CW radars [2], [8].
The application of such radar type in health monitoring includes a novel method proposed to simultaneously
extract heart rate and breathing information from echo
signals [10]. Another UWB impulse radar used for human sensing application is presented in [21]. This UWB
radar utilizes the same carrier frequency of 3 GHz used
in the CW radar described in the previous subsection.
In fact, the CW and the UWB radars of this work shared
fundamental elements such as the transmitter and receiver antennas, the power amplifier, the LNA and the
mixer for cost-efficiency. In this UWB radar, a 700 ps
Gaussian pulse is modulated with a 3 GHz carrier using
a mixer and channeled through a high gain power amplifier before being transmitted via a wideband Vivaldi
antenna. On the receiver side, an eight-element array
collects the signal, with one channel selected at a time
using a switch. Next, the signal is passed through an
LNA to be down-converted into baseband. The output
of the coherent down-conversion is then filtered before being channeled into an amplifier. The output is
then sent for digitization based on the equivalent time
sampling strategy. The structure of this UWB radar is
shown Figure 7.
50

IEEE CIRCUITS AND SYSTEMS MAGAZINE

A more challenging work involving the detection of
heartbeats originating from multiple stationary targets
at equal distances from an UWB radar is presented by
[37]. An algorithm to separate the fundamental frequency of the heartbeat from its harmonics was proposed
and analyzed numerically, prior to its experimental
validation using two targets. Results from this experiment were then compared with camera-based results.
Next, an algorithm was proposed to extract heart rate
from respiration signal in [38] using an UWB impulse
radar. Different experimental scenarios were performed
to prove the validity of this algorithm. Besides that, an
auto-correlation method was explored to detect random
body movements during experiments. The algorithm
was applied to the echoes reflected from stationary and
non-stationary targets. Meanwhile, the effects of speech
and hand movements on the measurement of respiration signal using an UWB radar was studied in [39]. It
can be observed that the noise from these activities affected the accuracy of the target signal.
In terms of hardware development, a 55 nm CMOS
SoC-based pulsed radar was developed in [4] for vital
sign detection. The block diagram of the SoC radar system is shown in Figure 9. Successful detection of the radar signals at 5 m and 9 m distances were also reported
in this work. In [40], a universal software radio peripheral platform (USRP-2954R) was used to implement the impulse radar system illustrated in Figure 10. This system
aims to detect displacement and vibration accurately
in real time. The time domain cross correlation ranging was performed using an FPGA. Meanwhile, the work
in [44] focuses on the signal processing of a low power
wireless CMOS impulse radar sensing system. A new
reconstruction methodology of the compressive sensing algorithm was proposed. It was implemented on an
FPGA and can support real-time human detection. Next,
in [45], a signal processing platform for UWB radar used
for analyzing human breathing was presented. This platform analyzes new features of human breathing, which
have not been investigated using radar systems. Examples of these features are inspiration and expiration
speeds, respiration intensity and holding ratio. To do so,
a new respiration signal model known as the four segments linear waveform (FSLW) respiration model was
proposed, with early termination techniques. The radar
transceiver diagram is shown in Figure 11.
In [47], a new method using UWB impulse radar to
detect human heart signal is proposed. This method is
aimed at introducing a solution with low power consumption, low implementation complexity and considers the
safety of the target. Spectral analysis is performed to
minimize the effects of unwanted noise originating from
movements of the human body on the detection accuracy.
FIRST QUARTER 2021

IEEE Circuits and Systems Magazine - Q1 2021

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q1 2021