IEEE Circuits and Systems Magazine - Q1 2021 - 56

The different signal processing algorithms used in
the literature in the human vital sign detection application will also be discussed in the next section. The
development/selection of the right algorithm can affect
the detection from many angles such as detection accuracy, speed, implementation complexity etc.
IV. Signal Processing Algorithms
for Vital Signs Detection
Algorithms adopted in vital signs detection to process
and extract useful information, depend on their objectives and vary in complexity. The challenge that needs
to be addressed with the algorithms is the weak respiration and heartbeat signals, the heart signal being superimposed on the respiration signal, and the environment
being filled with noise such as clutter, body movements,
and other noise sources in the radar's environment.
Thus, an important feature of these algorithms must be
being capable of distinguishing RR from HR and distinguishing RR and HR from noise. This is crucial as abnormal RR and HR can be mistaken as noise [62].
Due to this, there have been a considerable number
of algorithms introduced to " clean " the received signal
from unwanted noise. Other algorithms tend to focus on
increasing the SNR, since HR and RR signals are very
weak. In addition to that, several other algorithms focus
on the separation of the HR from the RR signal, given
that the HR is much smaller than RR. The HR is generally superimposed onto the RR signal, resulting in very
sophisticated algorithms to separate and to eliminate
the HR intermodulation effect on the RR signal. Therefore, most of the successfully adopted algorithms in the

human vital sign detection application are computationally complex due to these stringent requirements.
They mostly involve matrix inversion or multiplication,
or both. Large sizes of data matrices are also involved,
thus affecting the processing speed, hardware complexity, power consumption, and possibly accuracy. Such requirements also remain the main driver for researchers
to develop new algorithms and detection methods, and
to employ new architectures and configurations in the
processing platforms.
Several algorithms for vital sign detection for radar
systems have been listed and categorized in Table 6.
The multilevel fast multi pole method (MLFM) along
with method of moment (MoM) algorithm were used
in [31] to implement a complex human electromagnetic
model on a CW radar. Calculations were performed using a 13-node GPU cluster. This is aimed at accelerating the calculation process, enabling the solution of this
large-sized problem. The MLFM is based on a grouping
concept to speed up the iterative solution of the linear
equation system of the conventional MoM. This grouping method significantly reduced the complexity of
MoM from O ^ N 3 h to O ^ N log N h, where N is the number
of unknowns corresponding to the number of edges in
the meshed object. Due to the large problem size of this
application, the time to complete the computation can
be too long. As a result, MLFM was parallelized using
high computing processor (GPU) cluster.
Another important algorithm is presented in [44]
where a novel reconstruction algorithm for compressive sensing is presented for UWB radar. It is a two-stage
orthogonal matching pursuit (OMP) reconstruction

Tx Element
Rx Element
SFCW Signal
Transmitter
Reference
Clock
System Control
Computer
Data Display

Local
Oscillator

Super Heterodyne
Receiver

Signal Processing
Unit

Figure 14. MIMO SFCW radar in [55]. Licensed under Creative Commons attribution license https://creativecommons
.org/licenses/by/4.0/
56

IEEE CIRCUITS AND SYSTEMS MAGAZINE

FIRST QUARTER 2021

https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/

IEEE Circuits and Systems Magazine - Q1 2021

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