IEEE Circuits and Systems Magazine - Q1 2021 - 57

algorithm, designed to reduce the complexity of the reconstruction process. This algorithm included a block
wise OMP estimation, weight updating and decision
mechanism, and finally, fine estimation. The flow chart
of the proposed algorithm is illustrated in Figure 15.
The complexity of the proposed two stage OMP is
much lower compared to the normal OMP algorithm.
The OMP via matrix inversion bypass (MIB) algorithm
can further reduce the OMP and the complexity of the
two-stage algorithm, as shown in Figure 16. The reported SNR value of the radar system was −20 dB.
Research on vital sign detection of single stationary
target to date has progressed well. On the other hand,
vital sign detection of multiple humans is still a challenge due to the mutual interference of multiple humans.
A new effort to tackle this challenge was introduced in
[55], in which an automatic detection algorithm which
combines CFAR, morphological filtering and clustering

was implemented on a UWB MIMO radar. This is intended for the vital sign detection from multiple human
targets, specifically to improve the detection of weak
signals reflected from them. This detection method is
composed of three main procedures: preprocessing,
imaging enhancements, and automatic detection and localization, as shown in Figure 17. CFAR was adopted to
automatically detect multiple vital signs with large differences in magnitude in low radar cross section (RCS)
environments. It uses a 2D sliding window to scan all
pixels in the enhanced image to search for possible vital
signs. Figure 18 is an illustrative diagram of this method.
One of the major problems in the vital sign detection
is in scenarios where the target is behind a wall or under a wrecked building. In such scenarios, reflections
due to the wall cause substantial loss of signal energy.
The remaining energy signal pass through the wall and
propagates towards the human target and then the

Table 6.
Comparison of algorithms used for vital sign detection.
Process
Gain/ SNR

Advantages

Disadvantages

Adopted to suppress the
unwanted harmonics
and intermodulation
interferences in the
spectrum

50 dB compared
to 8.4 dB when
using FFT and
46.6 dB when
using SSM
alone.

SSM method can improve
SNR by suppressing
harmonics. When combined
with other methods (such as
CSD and AD) it is proven to
achieve higher SNR values.

The combined SSM
with CSD or AD
algorithm is done
at the expense of
the complexity

MLFM and MoM

Implementation of
human electromagnetic
model to be used for
vital sign detection

N/A

Very accurate in modeling
the human for vital sign
detection

Demands high
computational
resources

[44]

Two stage OMP
and OMP via MIB

Designed to reduce
the complexity of the
reconstruction process
of compressive sensing

−20 dB

Less complex than
traditional OMP,
advantageous in reducing
computational complexity

Needs to be
implemented in
more practical
scenarios

[55]

Combined CFAR
and morphological
filtering and
clustering

Designed for the
detection of multiple
humans, specifically to
improve the detection of
weak signals reflected
from human targets

N/A

Proven in detection of
multiple vital sign detection
with large magnitude
difference in low signal to
clutter ratio scenarios

Implemented on
stationary targets,
more practical
scenarios need to
be tested.

[63]

SVD and moving
average

Adopted for clutter
reduction

N/A

Proven for clutter reduction
from received signal where
it divides the data into target
and clutter subspaces.

Computationally
expensive and
slow. SVDs require
care dealing with
missing data.

[64]

Random body
motion rejection
algorithm

Designed for
suppressing random
body movement and
separating HR from RR

Process gain
32.83 dB
SNR is 70 dB
and 56dB for
person and
chest surface,
respectively

The combination of CWT,
linear demodulation, DWT
techniques are proven
in suppressing random
movement and providing
accurate results.

Combination of the
algorithms is high
in complexity as
it involves many
steps.

Algorithm

Purpose

[2]

SSM-complex
signal demod.
SSM-Arctangent
demod.

[31]

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