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Citation: Soumya, A.; Krishna
Mohan, C.; Cenkeramaddi, L.R.
Recent Advances in mmWave-Radar-
Based Sensing, Its Applications,
and Machine Learning Techniques:
A Review. Sensors 2023,23, 8901.
https://doi.org/10.3390/s23218901
Academic Editor: Antonio Lázaro
Received: 25 August 2023
Revised: 6 October 2023
Accepted: 21 October 2023
Published: 1 November 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Review
Recent Advances in mmWave-Radar-Based Sensing,
Its Applications, and Machine Learning Techniques: A Review
A. Soumya 1, C. Krishna Mohan 1and Linga Reddy Cenkeramaddi 2,*
1Department of Computer Science Engineering, Indian Institute of Technology, Hyderabad 502285, India;
cs21resch15003@iith.ac.in (A.S.); ckm@cse.iith.ac.in (C.K.M.)
2Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway
*Correspondence: linga.cenkeramaddi@uia.no
Abstract:
Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive
driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields
have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar
has several advantages that set it apart from other types of sensors. A mmWave radar can operate
in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization
than other traditional radars, and it has better range resolution. However, as more data sets have
been made available, there has been a significant increase in the potential for incorporating radar
data into different machine learning methods for various applications. This review focuses on key
performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning
techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of
the various working bands of mmWave radars, then moves on to various types of mmWave radars
and their key specifications, mmWave radar data interpretation, vast applications in various domains,
and, in the end, a discussion of machine learning algorithms applied with radar data for various
applications. Our review serves as a practical reference for beginners developing mmWave-radar-
based applications by utilizing machine learning techniques.
Keywords:
mmWave radar; mmWave radar applications; machine learning; industrial applications;
medical applications; automotive applications; military applications; computer vision
1. Introduction
The development of millimeter wave (mmWave) radar sensors during the past ten
years has been spurred on by numerous research applications, including civilian and
non-civilian applications [
1
,
2
]. With the latest improvements in chip technology and
lowered cost, the mmWave radar sensor has gained widespread popularity in a wide range
of applications. The mmWave radar system includes a transmitting antenna, a receiving
antenna, and a signal processing system to determine an object’s dynamic information, such
as range, velocity, and angle of arrival (AoA). The mmWave radar transmits a mmWave
signal into space by striking an object, and this signal gets reflected. The receiving antenna
captures the echo signal, which is then mixed with a transmitting signal to obtain an
intermediate-frequency (IF) signal. This IF signal is processed to obtain object information.
Various mmWave radars and working bands are shown in Table 1. mmWave radars operate
in the frequency range between 24 GHz and 300 GHz. Processing IF signals allow for the
measurement of an object’s range, velocity, and angle of arrival (AoA) [3].
Sensors 2023,23, 8901. https://doi.org/10.3390/s23218901 https://www.mdpi.com/journal/sensors
Sensors 2023,23, 8901 2 of 25
Table 1. Operational frequencies within the bandwidth spectrum.
Working Band (GHz) Bandwidth (GHz) Resolution
24 0.25 GHz 60 cm
77 4 GHz 3.75 cm
60 7 GHz 2.1 cm
94 2 GHz 7.5 cm
100 9 GHz 1.6 cm
300 40 GHz 0.375 cm
300 20 GHz 0.75 cm
The performance of signal processing is continually being improved along with hard-
ware components. The performance of mmWave-radar-based sensing is entirely based on
detecting mmWaves reflected by objects and subsequent signal processing. The perfor-
mance of the mmwave radar is independent of external lighting conditions and works well
even in low-light conditions and dazzling light. mmWave radar sensors are now widely
used in a variety of civilian applications, including obstacle detection, motion recognition,
localization, and tracking, owing to low-cost chip technology and improved reliability [
4
].
As a result, these improvements in radar technology and digital signal processing lead to
good accuracy in range and velocity estimation and better resolution in contrast with other
traditional radars. In addition to the benefits listed above, mmWave radar has superior
penetration capacity through various weather conditions like rain, fog, and snow.
Today’s radar-based sensing is more diverse. Applications range from civilian to
military and include a variety of automotive, industrial, and medical applications. Research
in the field is being refined as a result of technological advancements and more accurate
detection. To perceive the surrounding environment, mmWave radar sensors can easily
be integrated with other imaging sensors. Multi-sensor fusion effectively uses data from
multiple sensors to augment one another and improves the ability to extract detailed
information about targets under a variety of environmental and climatic conditions [
3
].
The mmWave radar is more often used in various applications when compared to other
sensors, such as RGB cameras, ultrasonic sensors, infrared sensors, and light detection and
ranging (LiDAR). Various applications demand the fusion of one or two sensors along with
mmWave radar [5].
The advantages mentioned above have increased the use and popularity of mmWave
radars. This article focuses on different state-of-the-art mmWave radars with key technical
specifications. The main focus of our study is on state-of-the-art mmWave radars, their
key performance metrics along with suitable applications, key measurements and their
interpretation, mmWave radar frequency selection criteria based on specifications and
application requirements, and machine learning techniques for mmWave-radar-based
sensing. Our review stands out as unique from other reviews due to its application focus
on the use of machine learning methods and provides a quick review of mmWave radar
models with specifications. The main significant aspects of this paper are highlighted in
Table 2, presenting how it differs from earlier surveys.
The remainder of this article is organized as follows: Section 2focuses on the per-
formance metrics of mmWave radar sensing. Section 3presents the measurements and
interpretation of the mmWave radar sensor. Various mmWave-radar-based applications
and machine learning techniques are focused on in Section 4. Finally, Section 5concludes
the article.
Sensors 2023,23, 8901 3 of 25
Table 2. Comparison of our work with earlier review articles.
Reference Year Focus Area SP MI Appl PM ML/DL
Tech. Comments
[6] 2011
Different mmWave technology specifications
7 7 3 7 7 Restricted to limited technologies
and data processing
[7] 2017
Focus on architecture, estimation techniques
3333 7 Restricted to advanced driver
assistance system
[4] 2019 Review of digital modulation and
interference mitigation methods 3 3 3 7 7 Restricted to automated
driving technology
[5] 2020
Working principles of vision sensors and
performance parameters for autonomous
systems
3 7 7 3 3 Restricted to autonomous systems
[8] 2021 Uses deep learning and fusion models 3 7 3 3 3 Deep fusion operations and
datasets need to be improved
[3] 2022 To determine working principles, data
representation methods, and challenges 3 7 3 3 7 Restricted to detection applications
[1] 2022
On various on-board sensors, hardware
components, software environments, and
machine learning algorithms
7373 3 Restricted to unmanned aerial
vehicle applications
Our Work 2022
Performance metrics of mmWave radar,
state-of-the-art mmWave radar models
available on the market, radar data
interpretation, applications of mmWave
radars, machine learning techniques for
mmWave radar
3333 3
Considers sensing applications
using mmWave radar sensor in
broad areas of science
and engineering
SP
—signal processing;
MI
—measurement interpretation;
Appl
—applications;
PM
—performance metrics;
ML/DL
Tech.—machine learning or deep learning techniques.
2. Performance Metrics in mmWave-Radar-Based Sensing
Currently, mmWave radars have become ubiquitous, with a wide variety of applica-
tions. The types of components in mmWave radars are shown in Figure 1. The synthesizer
generates the chirp, the transmitter transmits this chirp signal, the transmitted chirp is
reflected off the objects in front of the radar, and the reflected signal is received via the
receiver antenna. The RX signal and TX signal are mixed to generate the resultant IF signal.
By processing the IF signal, all the key parameters can be estimated. When multiple objects
are in front of the radar, one Tx chirp generates multiple reflected chirps from different
objects, and the IF signal will have multiple tones corresponding to each of the reflections.
The purpose of the analog-to-digital converter (ADC) is to digitize the IF signal. Fast
Fourier transform (FFT) is performed on this digitized IF signal to obtain the range profile.
Early radar systems were primarily utilized for navigation and object detection at
short ranges. They were used in maritime navigation, for example, to detect other vessels or
impediments in foggy circumstances. However, advancements in semiconductor technol-
ogy and processing led to advancements in the development of advanced radar systems [
7
].
The mmWave radar provides better antenna miniaturization than other traditional radars.
Broadband radio frequency (RF) signals allow for the better resolution capability of the
radar, which is the key to many high-performance automotive, medical, and industrial
applications. Additionally, their enhanced portability and affordable chip have made them
suitable for usage in various day-to-day applications.
A mmWave frequency-modulated continuous wave (FMCW) radar accurately esti-
mates the range and velocity of multiple targets from the radar sensor without the need
for more transceivers [
9
]. For the accurate estimation of the range and velocity of multiple
targets, one TX antenna and one RX antenna are sufficient. However, the angle of arrival
(AoA) estimation of multiple targets demands more hardware resources, such as more
TX antennas and more RX antennas. The greater the number of transceiver antennas, the
better the AoA estimation performance will be. However, while detecting objects using
one mmWave radar, signals from other nearby radars cause interference, which eventually
degrades the performance of the mmWave radar. However, there are some mitigation
techniques, though it is still challenging, and this is still an ongoing research topic.
Sensors 2023,23, 8901 4 of 25
To select the suitable mmWave radar based on the application, the following perfor-
mance metrics play a crucial role [10,11]:
•
Range —The range is estimated by analyzing the frequency content in the IF signal. As
shown in Figure 2a, transmitted and received chirps as a function of time for a single
object detected. It can be observed that the received chirp is a time-delay (
τ
) version
of the transmitted chirp.
The time delay can be measured as:
(τ) = 2R/C
where Ris the distance to the detected object and Cis the speed of light.
The initial phase of the IF signal is φ0
(φ0) = 4πR/λ
The range is computed as:
R=C×fIF /2S
The maximum range is decided via the sampling frequency of this IF signal. The larger
the sampling frequency, the better the maximum range capacity of the radar will be.
The other deciding factor for the maximum range is the transmission power. There is
no hardware constraint on the accurate range estimation of multiple targets. One TX
antenna and one RX antenna are sufficient.
Rmax =FsC/2S
Another fundamental limitation for maximum range comes from the transmitting
power:
Rmax = ((σPtGTX GRX λ2Tmeas)/(4π3SNRminkTF) ) 1
4
where
Pt
is output power of device;
GTX
is the TX antenna gain,
GRX
is the RX
antenna fain;
σ
is the radar cross-section of the target (RCS);
Tmeas
is total measurement
time; SNR is the signal-to-noise ratio;
k
is the Boltzmann constant; and
T
is antenna
temperature.
•
Velocity—mmWave radar estimates the velocity of multiple targets using the phase
difference between IF signals, as illustrated in Figure 2b. There is no hardware
constraint for the accurate velocity estimation of the multiple targets.
One TX
antenna
and one RX antenna are sufficient.
The phase difference is derived as
∆Φ
= 4
πVTc
/
λ
, where
Tc
is the chirp duration
time. The velocity is computed as:
V=λ∆Φ/4πTc
The measurement is unambiguous only if |
∆Φ
| <
π
. We can derive that vs. <
λ
/4
Tc
.
The maximum possible velocity estimation depends on how fast chirps can be trans-
mitted.
Vmax =λ/4Tc
•
Angle of arrival—Estimating the angle of arrival of one object requires at least one
TX antenna and two RX antennas as shown in Figure 3. The greater the number of
RX antennas, the better the AoA estimation performance will be. By using MIMO,
more transceiver elements can be made with a limited number of TX and RX antennas.
However, accurate AoA estimation of multiple targets demands more hardware
resources, such as more TX and RX antennas.
The phase difference between the IF signals of the two receivers is derived as:
(∆Φ) = 2π∆R/λ
Sensors 2023,23, 8901 5 of 25
Under the assumption of a planar wavefront basic geometry
∆
R = d
sin(θ
), where d
is the distance between the receiving antennas. The angle of arrival
θ
is computed
from ∆φ
(θ) = sin−((λ∆ Φ)/(2πd))
The unambiguous measurement of the angle of arrival requires that (∆Φ) < π.
This leads to:
(θmax ) = sin−((λ)/(2d))
•
Range resolution—The shortest distance at which two objects can become close while
still being detected as two distinct objects via radar. The smaller this distance, the
better the resolving capability of the radar will be. A radar with greater RF bandwidth
gives better range resolution.
The smallest frequency differences of an IF signal are related to the chirp duration,
Tc
.
(∆f)>1
Tc
where Tcis the observation interval or chirp duration.
(since(∆f) = S2∆R
c)
(∆R)>c/2STc=c/2B(sinceB =STc)
Range resolution depends only on the RF bandwidth swept by the chirp.
Range resolution =C/(2∗B)
where C is the speed of light, and B is the radar bandwidth. For example, a bandwidth
of 4 GHz gives a range resolution of 3.75 cm.
•
Velocity resolution—The smallest velocity difference the two targets can have while still
being detected via radar as two distinct targets with two distinct velocities. Velocity
resolution can be improved by increasing the frame time. A frame consists of a number
of series of chirps.
(∆Φ) = 4πV Tc/λ
One can mathematically derive the velocity resolution (Vres) if the frame period
Tf= NTc.
V>Vres = λ/(2 Tf)
Vres =λ/(2Tf)
where Tfis the frame time.
•
Angle of arrival resolution—The angle of arrival resolution is the smallest angle that can
be formed between two targets and radar while still being detected via the radar as
two distinct targets. The smaller the angle of arrival resolution, the better the radar’s
resolving capability. Extending the number of antennas (both TX and RX) improves
the AoA resolution.
(θ(res)) = 2/NRX
All the performance parameters, such as range, velocity, angle of arrival, range res-
olution, velocity resolution, AoA resolution, maximum range, maximum velocity, and
maximum AoA, are analytically tabulated in Table 3.
Sensors 2023,23, 8901 6 of 25
Figure 1. Architecture of the mmWave radar sensor.
Figure 2. (a) IF signal and (b) chirp frame [12].
Figure 3.
(
a
) Two antennas are required to estimate AoA and (
b
) maximum angular field of view [
12
].
Table 3. Performance metrics of mmWave radar.
Range Velocity Angle of arrival
R=(C×fIF )/2SV= λ∆Φ/4πTcθ= sin−((λ∆Φ )/(2πd))
Range resolution Velocity resolution Angle of arrival resolution
dres = C/2BVres = λ/(2 Tf)θres =2/NRX
Max range Max velocity Max angle of arrival
dmax = FsC/2SVmax= λ/4 Tcθmax= sin−((λ/2 d))
R
—target’s range from the radar;
V
—target’s Velocity;
θ
—target’s angle of arrival;
C
—speed of light;
S
—slope of
chirp, B/
Tc
;
∆φ
—phase difference between IF signals;
Tf
—frame time,
NTC
;
B
—chirp RF bandwidth;
fIF
—IF
signal frequency;
Fs
—sampling rate of the IF signal;
Tc
—chirp duration time;
λ
—wavelength of the chirp signal;
N—number of chirps; NRX —number of receiving antennas; d—distance between receiving antennas.
Sensors 2023,23, 8901 7 of 25
The following are important factors to take into account when choosing a mmWave
radar [10,11]:
•
Sensor type—The RF bandwidth, IF bandwidth, ADC sampling rate, range resolution,
velocity resolution, and AoA resolution are important key factors in deciding the
performance. mmWave radar sensors are broadly categorized into three sensor types:
long-range, medium-range, and short-range radars, as listed in Table 4. The appropriate
sensor should be chosen depending on the application demands and needs.
•
Frequency and bandwidth—It is advantageous to have a high-frequency sensor; it uses
a low antenna size and gives a better angular resolution. High bandwidth offers a
high-range resolution. A 77 GHz frequency radar with a 4 GHz bandwidth gives a
range resolution of 3.75 cm. Popular mmWave radar models with specifications are
listed in Table 5.
Table 4. Suitable mmWave radar types with respect to the applications.
Type Range (m) Bandwidth
(MHz)
Azimuth View Angle
(deg.)
Elevation View Angle
(deg.) Applications
Long-range radar 10–250 600 ±15 ±5 Autonomous cruise control
Medium-range radar 1–100 600 ±40 ±5Lane change assistance
system, collision mitigation
Short-range radar 1–30 4000 ±80 ±10 Blind spot detection, parking
assistance
Table 5. Popular mmWave radar models and their specifications.
Reference Model Name Range
(m)
Working Band
(GHz)
Azimuth Field
of View (deg◦)
Elevation
Field of
View (deg◦)
Chip
Memory
(MB)
User Interface and Connectivity
[13] TI-IWR6843AOPEVM 180 60 GHz–64GHz ±120 ±120 1.75 TMMWAVEICBOOST, DCA1000, I2C,
LVDS, QSPI, SPI, UART
[14] TI-IWR1843 180 76 GHz–81 GHz 100 ±15 2 CAN, LVDS, QSPI, I2C, SPI, UART,
CSI-2
[15] Delphi ESR 174 76 GHz ±10 ±45 - CAN
[16] NavTech CIR204-h 200 76 GHz–77 GHz 360 - - 1 Gbps Ethernet
[17] TI-AWRL6432 250 57 GHz–64 GHz ±18 - 1 QSPI, PWM, I2C
[18] BoschLRR3 250 77 GHz 30 - - CAN
[19] Continental ARS408-21 250 77 GHz ±9 14 - CAN 500 kbps
[20] Continental SRR600 >180 76 GHz–81 GHz ±90 ±40 - Ethernet, CAN-FD
[21] TI-AWR1843AOPEVM 150 76 GHz–81 GHz 140 140 2 DCA1000EVM, CAN, CAN-FD
[22] TI-AWR1642 100 76 GHz–81 GHz ±60 ±10 1.5 CAN, CAN-FD, SPI, I2C, UART
[23] TI-IWR1642 150 76 GHz–77 GHz ±100 ±15 1.5 CAN, CAN-FD, QSPI
[24] NXP-TEF810X 250 76 GHz–81 GHz ±18 - 1.5 LVDS, CSI2
[25] NXP-SAF85xx 250 76 GHz–81 GHz ±18 - 5.5 SGMII Ethernet, dual CAN FD
[26] NXP-TEF82xx 250 76 GHz–81 GHz ±18 - 0.576 CSI-2, LVDS
[27] Continental-ARS540 300 76 GHz–81 GHz ±60 ±60 1 CAN, Ethernet
[23] TI-IWR1443 60 77 GHz–79 GHz ±65 ±15 1.5 LVDS, DCAN, QSPI, CSI2
[28] TI-AWR1443 60 76 GHz–81 GHz ±65 ±15 1.5 LVDS, DCAN, QSPI
[29] TI-IWRL6432 60 57 GHz–64 GHz ±65 ±15 1 I2C, SPI, UART, QSPI
[30] Continental-ARS4-A 250 77 GHz ±75 ±20 1 CAN, Ethernet
[31] TI-AWR2243BOOST 150 76 GHz–81 GHz ±90 ±90 1.5 SPI, UART, I2C, CAN-FD
[32] TI-AWR1243 250 76 GHz–81 GHz ±60 ±15 1 SPI, MIPI-CSI2, UART
[33] NXP4D-S32R45 300 77 GHz ±56 ±56 1 PCIe, Ethernet, CAN-FD
[34] RDK-S32R274 200 79–81 GHz ±30 ±30 1 Ethernet, CAN-FD
Sensors 2023,23, 8901 8 of 25
•
Accessories—As the needs of mmWave radars change, upgrading the firmware and
software becomes necessary. Hence, choosing a manufacturer that can offer stable
software updates is important. mmWave radars have been integrated with Lidars
in [
35
,
36
] to provide better results; additionally, radars fused with cameras or infrared
sensors are studied in [
37
–
39
]. Texas Instruments (TI) introduced a commercial radar,
TDA3x, board for radar camera fusion to provide effective tracking and detection
applied in [
40
]. There is a discussion of fusion techniques in [
41
,
42
], such as low-level
and feature-level fusion.
3. Measurements from mmWave Radar Sensor and Interpretation of the Data
3.1. Range Profile
A sample range profile measured using the millimeter wave radar sensor is illustrated
in Figure 4a. Taking the fast Fourier transform (FFT) of an intermediate-frequency signal
yields the range profile. This range profile depicts the relative reflected power from the
targets as a function of range. The target is indicated via the peaks in the range profile.
Targets with a large cross-section reflect more power, producing a stronger peak. Targets
located near the radar produce a strong peak in the range profile.
Figure 4. (a) Range profile and (b) range–Doppler heatmap.
A range–Doppler heatmap displays multiple targets with their speeds as a function
of range, as shown in Figure 4b. Stationary targets have zero Doppler while moving
targets have a range–Doppler map with non-zero Doppler values. This essentially provides
dynamic information about the targets, such as their velocity and range.
3.2. Range–Azimuth Heatmap
The range–azimuth heatmap in Figure 5a is intended to display radar cube information
corresponding to a zero Doppler bin for every combination of range and angle bin. It
essentially gives the location information about the targets with respect to radar in a
Cartesian coordinate system. The colored signal power representation within the plot
shows the range and angle coordinate points where the targets are located.
3.3. Three-dimensional Scatter Plot
The detected targets are displayed in 3D space by selecting a non-zero elevation
resolution as the antenna configuration, as shown in Figure 5b.
Sensors 2023,23, 8901 9 of 25
Figure 5. (a) Range–azimuth heatmap and (b) 3D scatter plot.
4. Applications of mmWave Radars and Machine Learning Techniques
mmWave radars are used in a variety of applications, as illustrated in Figure 6. These
include automotive applications, industrial applications, military applications, medical
applications, robotics and automation applications, civilian applications, and security and
surveillance applications.
Figure 6. Applications of mmWave radar sensing.
Sensors 2023,23, 8901 10 of 25
4.1. Automotive Applications
Automotive applications use mmWave radar sensors to accurately localize and mea-
sure the radial range, velocity, and AoA of moving objects. The applications include
adaptive cruise control, autonomous emergency braking, blind spot detection, lane change
assistance, front cross-traffic alert, rear cross-traffic alert, automated parking, body/chassis
applications, and in-cabin applications. The relative positioning of vehicles has been
estimated using mmWave radar in [
43
]. Vehicle detection in advanced driving assistant sys-
tems using automotive radar with range–azimuth–Doppler dimensions is studied in [
44
].
A 2D car detection system for autonomous driving applications is studied in [
45
]. The
ability of an autonomous vehicle to perceive and comprehend its surroundings is studied
in [
46
]. The use of mmWave radars for vehicle detection in self-driving applications is
studied in [
47
]. The use of mmWave radar sensor and vision sensor fusion for obsta-
cle detection in autonomous driving is studied in [
48
]. To obtain high accuracy in new
advanced driver-assistance systems (ADASs), mmWave radars have been used in vehi-
cles [
49
], as shown in Figure 7. A 60 GHz mmWave radar has been used to reduce driver
distractions with real-time hand gesture recognition instead of touchscreens and wearable
components [
50
]. Machine-learning-based hand gesture recognition is studied in [
51
] using
CNN and LSTM. People occupancy detection in a vehicle has been implemented utilizing
mmWave radar [
52
]. Furthermore, contactless non-intrusive vehicle occupant detection
is studied in [
53
], and autonomous navigation by predicting vehicle location utilizing
mmWave radar is studied in [
54
]. Radar sensors placed at the front and rear corner of the
car that form beams for front and rear blind spot detection, as well as for cross-traffic alert,
are demonstrated in [
55
]. mmWave radar became a solution for ground-based traffic moni-
toring, and the management of both terrestrial and aerial vehicles using angle estimations
in [
56
]. The detailed automotive applications and associated radar details are tabulated in
Table 6.
Figure 7. Advanced driver-assistance system [57].
Sensors 2023,23, 8901 11 of 25
Table 6. Automotive applications using popular radars.
Reference Year Application Radar Used
[58] 1997 Intelligent cruise control with collision warning FMCW (76 GHz–77 GHz)
[59] 2017 Blind spot detection and warning system AWR1843 (76 GHz–77 GHz)
[60] 2017 Automated emergency breaking TI-AWR1243 (76 GHz–78 GHz)
[60] 2017 In-car occupant detection TI-AWR1642 (76 GHz–81 GHz)
[61] 2017 Driver vital sign monitoring TI-AWR1642 (77 GHz)
[62] 2018
Automotive body and chassis sensing applications
TI-AWR1642 (77 GHz)
[50] 2018 In-car controlling with gestures FMCW-mmWave (60 GHz)
[63] 2019 Automated parking system TI-AWR1843 (77 GHz)
[64] 2020 Lane change assistance with obstacle detection TI-AWR1843AOPEVM (77 GHz)
[65] 2020 Parking assistance with obstacle detection TI-AWR1642BOOST (77 GHz–81 GHz)
[66] 2020 Debris detection for automotive radar mmWave (76 GHz–81 GHz)
[67] 2021 Automotive vehicle detection in parking lot
TI AWR2243BOOST-MIMO (76 GHz–81 GHz)
[65] 2022 Motor cycle safety and Blind spot detection TI-AWR1843AOP (76 GHz–81 GHz)
[55] 2022 Automotive corner radar for cross traffic alert TI-AWR1843EVM (76 GHz–81 GHz)
4.2. Industrial Applications
mmWave radars have become popular as they provide precise measurements that
are useful in industrial applications. Industrial applications include level sensing of fluids,
volume identification for solids, infrastructure systems, surface quality assessment in
production industries, and vibration monitoring. Utilizing mmWave radar for automatic
crack detection to distinguish between cracked and uncracked ceramic tiles and for quality
control of packaged ceramic tiles is presented in [
68
]. In industrial processes, to have
control over the usage of liquid and identify leakages, mmWave radars have been utilized
for accurately measuring fluid levels in tanks, as presented in [
69
]. In [
70
], a framework
for accurate material identification for six different materials using mmWave radar is
presented. In addition, the volume of the materials has been determined. Using low-power
transmission signals and reflections with a non-line-of-sight (NLOS) method for detecting
moving objects has been studied in [
71
]. This study includes a model for the echo signal
of the NLOS target by considering the multipath effect and the weak target echo signal
issues. The detection and classification of gases and aerosols have been implemented in [
72
].
Detecting the vibrational target objects by modifying shaking frequencies and assessing
the performance is studied in [
10
]. The monitoring of the mass flow of pneumatically
transported bulk materials using mmWave measuring is presented in [
73
]. mmWave radars
are useful in metal production industries, where they require the precise measurement of
slabs of copper, steel, and aluminum in production. In rolling mills, mmWave radar sensor
technology is useful to provide accurate measurements, even in smoky, hot, steamy, and
dusty conditions, as shown in Figure 8. The detailed industrial applications along with the
radar utilized are tabulated in Table 7.
Sensors 2023,23, 8901 12 of 25
Figure 8. Width measurement in a cold and hot rolling mill [74].
Table 7. Industrial applications using popular radars.
Reference Year Application Radar Used
[75] 2006 Surface sensing mmWave sensor (29.72 GHz–37.7 GHz)
[76] 2013
Measuring the liquid level and interface sensing
mmWave Doppler sensor (77 GHz)
[68] 2015 Crack detection in ceramic tiles V-Band Imaging Radar (60 GHz)
[69] 2017 Fluid level sensing TI-IWR1443 (77 GHz)
[77] 2018 Material classification
FMCW radr with Infineon’s DEMO-BGT60TR24
sensor (60 GHz)
[78] 2018 Motion detection and intersection monitoring IWR6843 60 GHz radar
[79] 2019 Foam detection in chemical applications IC with mmWave ssensor (80 GHz)
[10] 2020 Obtaining the performance on detecting
vibrational targets FMCW 80 GHz sensor integrated on SiGe chip
[80] 2022 Eavesdropping and spying on phone calls TI-AWR1843BOOST (77 GHz)
[70] 2022 Material identification TI-IWR1443 FMCW (77 GHz–81 GHz)
4.3. Medical Applications
Medical applications for mmWave radars have also gained importance due to their
sensitive detection capability and the penetration of mmWave signals in biological tissues.
Using mmWave radar, various glucose concentration levels in blood samples to distinguish
the healthy or diabetic have been studied in [
81
]. In [
82
], contactless breathing rate and
heart rate monitoring of patients using mmWave were implemented, as shown in Figure 9.
mmWave radar sensors have been used for monitoring vital signs via non-contact means in
a robust way [
83
]. The use of mmWave radar in real-time human motion behavior detection
is presented in [
84
]. The recognition of multiple patient behaviors has been simultaneously
studied utilizing mmWave radars in [
85
]. The real-time detection and tracking of human
skeletal positions for patient monitoring is studied in [
86
]. Considering body movements as
micro-motion parameters for real-time fitness tracking via non-contact means has been stud-
ied in [
87
]; additionally, fitness tracking by classifying and counting exercises is presented
in [
88
]. The detection of sleeping pose identification utilizing mmWave radar has been
implemented in [
89
]. For skin diagnosis applications, mmWave radar has been utilized
in [
90
]. The utilization of a wearable radar sensor for continuous blood pressure monitoring
is presented in [
91
]. Detailed medical applications utilizing mmWave radars are tabulated
in Table 8.
Sensors 2023,23, 8901 13 of 25
Figure 9. Contactless patient monitoring application [82].
Table 8. Medical applications with popular radars.
Reference Year Application Radar Used
[81] 2018 Blood glucose level detection FMCW-XENSIV (60 GHz)
[85] 2019 Multiple patients behavior detection TI-AWR1642BOOST (77 GHz)
[90] 2020 Skin cancer detection Designed sensor (77 GHz)
[87] 2021 Contactless fitness tracking TI-IWR1642 (77 GHz–81 GHz)
[82] 2022
Contactless monitoring of patients and elderly people alone
IWR6843AOPEVM (60 GHz–64 GHz)
[92] 2022 Measuring systolic blood pressure TI-IWR6843AOP (60 GHz–64 GHz)
[93] 2022 Vital sign measuring TI-IWR1443 (77 GHz–81 GHz)
[94] 2022 Health monitoring with posture estimation TI-IWR6843 (60 GHz–64 GHz)
[95] 2022 Blood pressure monitoring TI-AWR1843 (77 GHz–81 GHz)
[96] 2022 Cardiorespiratory rate monitoring Commercial FMCW (122 GHz)
[97] 2022 Galvanic skin test to assess mental acuity and stress levels TI-AWR1843 (77 GHz)
[98] 2022 Automated heart rate and breathing rate monitoring TI-AWR1443BOOST (77 GHz)
4.4. Robotics and Automation Applications
Robotics applications include both indoor and outdoor environments. Detecting trans-
parent objects such as glass walls is very important in autonomous navigation. mmWave
radars reliably detect glass walls [
99
]. They are also quite reliable as ground-speed radars in
agricultural and warehouse robots [
99
]. Using the same ground-speed radar, it is possible
to sense the surface edges if the radar is mounted in front of it, facing toward the ground.
Safeguards around robotic arms are another important field wherein mmWave radar plays
a vital role. Mapping and navigation is another important application where mmWave
radars are used in indoor environments. Robotic applications for human path tracking
and collision avoidance are explored utilizing mmWave radars [
100
]. The detection of
obstacles and avoiding collision in a 360-degree path in robotics using mmWave radars
is demonstrated in [
101
], as shown in Figure 10. Incorporating an antenna on package
sensors with a wider field of view in both azimuth and elevation helps in the intelligent
sensing of transparent objects and dark objects, which is studied in [
102
]. Glass walls and
the materials behind them can be detected using mmWave radar sensors [
99
]. Detailed
robotics and automation applications and associated radar details are tabulated in Table 9.
Table 9. Robotics and automation applications with popular radars.
Reference Year Application Radar Used
[102] 2019 Intelligent robot for transparent object sensing IWR6843 (60 GHz)
[103] 2020
Robot-mounted mmWave radar for tracking heart rate
IWR6843 (62 GHz)
[54] 2020 Predicting autonomous robot navigation FMCW (77 GHz)
[101] 2020 Collision detection and avoidance IWR6843 (60 GHz)
[104] 2020 mmWave radars as safe guard robots IWR6843 (60 GHz)
[100] 2021 Automated indoor navigation and path tracking AWR6843 (77 GHz)
[99] 2021 Glass wall and partition detection IWR1443BOOSTEVM (77 GHz)
Sensors 2023,23, 8901 14 of 25
Figure 10. Robotics application [100].
4.5. Security and Surveillance Applications
mmWave radars are useful in security and surveillance applications because they
can detect moving objects and obstacles in low-light conditions. In particular, personal
screening and maintaining security aspects are discussed in [
105
]. mmWave radars are
being used in air traffic control systems and low-altitude space surveillance applications
to detect and display the position of aerial vehicles. Aerial vehicle activity monitoring
with radar range and angle measurements is studied in [
106
]. Airborne radars for obstacle
avoidance, landing aids, automotive radars for collision avoidance, and driving safety
support are studied in [
107
]. In [
108
], airborne surveillance with navigational aid on the
ground using mmWave synthetic aperture radar (SAR) is implemented, which tracks the
actual flight route and records it. Unmanned aerial vehicle (UAV) detection with respect
to a range of up to 40 m using low-cost mmWave sensors is reported in [
2
]. In airports
and other sensitive places, mmWave radars are utilized in the identification of intrusions
in [
109
]. Vehicle detection and tracking in traffic monitoring applications with a range
greater than 100 m are shown in Figure 11. A richer radar point cloud representation for a
traffic monitoring scenario is shown in [110]. People tracking using radar applications for
consumers in indoor and outdoor environments is presented in [
111
]. Furthermore, channel
tracking for a vehicular communication system is studied in [
112
]. Detailed security and
surveillance and civilian applications and associated radar details are tabulated
in Table 10.
Sensors 2023,23, 8901 15 of 25
Figure 11. Traffic monitoring application [113].
Table 10. Security and surveillance and civilian applications with popular radars.
Reference Year Application Radar Used
[114] 2006 Power line prediction in helicopter rescue mmWave radar (94 GHz)
[115] 2008 mmWave radars for safe helicopter landing Radar module with 94 GHz
[116] 2010 Providing indoor security of short range mmWave SAR (77 GHz)
[117] 2010 Debris detection on airport runways mmWave radar (73 GHz–80 GHz)
[118] 2013 Concealed threat detection W-band (75 GHz–110 GHz)
[108] 2015 Surveillance imaging applications MIRANDA radar (35 GHz and 94 GHz)
[113] 2018 Traffic monitoring IWR1642EVM 77 GHz radar
[119] 2019 Human target detection, classification,
tracking ISM band (24 GHz MIMIC)
[120] 2020 Tracking of malicious and hidden drones mmWave (77 GHz)
[121] 2021 Ego-motion estimating in indoor
environments TI-AWR1843BOOST (76 GHz–81 GHz)
[2] 2021 Unmanned aircraft system detection and
localization AWR1843 Boost (76 GHz–81 GHz)
[106] 2021 Aerial vehicle locating and air traffic
management AWR1843 (76 GHz–79 GHz)
[122] 2021 3D human skeletal pose estimation TI-AWR1843 (77 GHz)
[123] 2023 Indoor positioning system IWR6843ISK (60 GHz–64 GHz)
4.6. Civilian Applications
mmWave radars have grown in popularity because they are robust to adverse weather
conditions and find many uses in the civilian sector. The creation of a drone setup by
integrating a mmWave sensor to detect power lines up to a 40-m range with improved
performance and fast detection is shown in Figure 12. Debris detection on airport runways,
early risk warnings for helicopters, power line detection in flying paths, and malicious
drone detection are some of the applications of mmWave radars. Detecting small foreign
Sensors 2023,23, 8901 16 of 25
objects on airport runways for a safe landing is studied in [
117
], as shown in Figure 13.
mmWave radars reliably detect high-voltage invisible power lines in snowy and inclement
weather. Risk avoidance with early warnings from mmWave sensors to rescue helicopters
is discussed in [114].
Figure 12. Power line communication [124].
Figure 13. Debris detection on airport runways [125].
In [
120
], a cooperative radar sensing network is implemented for tracking small,
hidden, unauthorized unmanned aerial vehicles (UAVs). Micro-UAV detection for defense
applications using 24 GHz mmWave radars is presented in [
126
]. The classification of birds
and drones using radar micro-Doppler signatures is presented in [
127
]. The detection and
mitigation of GPS spoofing for drones is explored in [
128
]. Aircraft runway extraction in
low-visibility conditions for a safe landing is investigated in [
129
]. Furthermore, in [
115
], the
use of mmWave radars for the safe landing of helicopters in inclement weather conditions
based on height drift data is studied. Another interesting application is implemented
in [
130
], which utilizes a CNN architecture to generate radar maps for recognizing and
classifying various real road images captured from gravel, mud, and river surfaces.
Sensors 2023,23, 8901 17 of 25
4.7. Other Applications
Object detection is one of the earliest applications of mmWave radar, which extends
to human fall detection, as presented in [
131
]. Hand gesture recognition for user interac-
tions with computers is studied in [
132
]. A model for long-range gesture recognition is
investigated in [
133
]. Furthermore, the real-time recognition of macro-gestures is presented
in [
134
]. Human pose estimation through occlusions and walls is explored in [
135
]. The
implementation of mmWave harmonic sensors to track small insects is conducted in [
136
].
The use of 61 GHz mmWave radar for human face classification is investigated in [
137
].
Non-contact skin sensing for analyzing human emotional arousal and stress status using
mmWave radars is implemented in [
97
]. The use of mmWave radar combined with GNN
and LSTM for human activity recognition is explored in [138].
In addition to the aforementioned areas, mmWave radars are also used in underground
mining with range measurements [
139
], spying on phone calls [
80
], efficient soil moisture
sensing [
140
], micro-action recognition systems [
141
], mmWave radar and audio signal
fusion for speech recognition [
142
]. Recent advances in sensor technology, combined
with machine learning techniques, have also enabled new applications for mmWave radar
sensors to be developed.
The use of mmWave radar combined with machine learning algorithms has grown
in popularity in recent years. As shown in Table 11, we provide an overview of machine
learning algorithms that are widely used in computer vision and related fields and have
been applied to radar signal processing. The applications of machine learning include
object detection, classification, clustering, and tracking, utilizing radar data. IF signals from
radars contain a predefined set of target features, and these IF signals are used in machine
learning models to make subsequent predictions. However, deep learning algorithms are
based on multiple layers of neural networks to learn high-level feature representations
from input radar IF data, which are then used to make intelligent decisions.
Furthermore, many research works are in progress that input radar signals into var-
ious deep learning techniques for object detection, such as those in [
45
,
143
–
145
]; object
classification, such as those in [
146
,
147
]; object segmentation, such as those in [
46
,
148
]; and
multi-class target classification, such as those in [
149
], using mmWave radar range–angle
images. Furthermore, target classification using the range FFT of a mmWave radar’s sta-
tistical features is studied in [
150
]. The utilization of various deep learning techniques
and micro-Doppler patterns from radar data for object classification is explored in [
151
].
Multi-person identification with distinct micro-Doppler signatures is studied in [152].
Sensors 2023,23, 8901 18 of 25
Table 11. Machine learning techniques for mmWave radar sensing.
References Year Method Application Comments
[88] 2016 CNN, data transformation
techniques Fitness tracking
1. Can classify different exercises with 95.53 accuracy and is capable
of counting repetitive exercises.
2. Counting repetitive exercises improves accuracy.
[132] 2016 Random forest algorithm Hand gesture recognition
1. RF offers 86% per-gesture accuracy with raw data.
2. RF with Bayesian Filter offers 92% per-gesture accuracy with
raw data.
[84] 2019 Convolution neural
network (CNN) Human behavior detection
1. Point cloud data are processed using the CFAR algorithm.
2. The usage of micro-Doppler information on human activities with
CNN produces an accuracy above 99%.
[131] 2019 NN is compared with SVM,
DT Fall detection
1. Attains 98% accuracy with NN backpropagation.
2. Evaluated on only three possible human positions with coordina-
tion points.
[153] 2019 CNN, ConvLSTM, RF Received power prediction
1. Power prediction from image works effectively with rotated 3D
CNN, and spatiotemporal features are predicted with a Random
forest algorithm.
2. Received power of 500ms with high accuracy and RMS errors less
than 1.0 is achieved.
[112] 2019 LSTM Channel tracking in
vehicular system
1. Accurate user channel prediction, and less overhead rate.
2. Usage of LSTMs is to predict the user channel based on past channel-
state information.
[45] 2019 PointNets 2D car detection 1. Using PointNets for classification with segmentation.
2. Mutli-class object detection needs to be investigated.
[46] 2020 CNN, RNN Scene understanding via
classification
1. CNN with grid maps as input for classifying static objects.
2. RNN with point clouds as input for classifying dynamic instances.
[47] 2020 DBSCAN, Faster R-CNN Vehicle detection
1. The proposed method performs better as it is a DBSCAN method
based on elevation resolution and also removes noise points using
filters.
2. Using Faster R-CNN achieves 96% accuracy by representing the tar-
get with the density of the point cloud.
[110] 2020 Point clouds, GMM Multimodal traffic
monitoring
1. GMM performs point cloud segmentation from sensor-collected
point clouds.
2. Can extend with DBSCAN for classifying more transportation
modes.
[48] 2020 SAF-FOC framework Obstacle detection
1. Feature-level fusion performs well compared with data-level and
decision-level fusion.
2. To cover 360◦coverage, the framework can be extended with multi-
ple sensors.
[86] 2020 CNN Detecting human
skeletal pose
1. Radar data to image representation with the help of depth, az-
imuth, and elevation information of reflection points to identify
skeletal position.
2. Proposed an architecture with significantly reduced computational
complexity with reused weights, and it also provided lower local-
ization error, such as 3.2 cm depth and 2.7 cm elevation.
[138] 2021 Graph neural network with
LSTM
Human activity recognition
and gesture recognition
1. Iteratively extracts the point cloud features and updates the graphs.
2. Excellent action recognition performance compared to
other methods.
[154] 2021 SVM Shape classification and
object detection
1. Uses SVM with RFB kernel to achieve an accuracy of 96%.
2. Comparatively less accuracy is obtained to classify multiple
target objects.
[98] 2022 CNN
Automatic monitoring of
heart rate and
breathing rate
1. Obtained 87% classification accuracy by forming low, average, high,
and a combination of six classes using CNN.
2. Removes the noise caused via vibrations and gives clear rate.
[70] 2022 CNN, K-nearest neighbor Material identification
1. K-NN uses two feature sets, while CNN uses the material’s distinc-
tive features for identifying the materials.
2. Enhanced classification accuracy of 98% in identifying the six mate-
rials at three different volume levels.
5. Conclusions
mmWave radar sensors have significant advantages compared to other sensors, mak-
ing them an ideal solution for a vast number of applications. This article discussed the key
performance parameters as well as the interpretation of radar measurements for mmWave
radar sensors. The most recent mmWave radar advances and cutting-edge mmWave radars
were thoroughly reviewed. The use of mmWave radar sensors was discussed in a variety
of applications, such as automotive, industrial, robotics and automation, medical, security,
and surveillance fields, as well as others. Finally, machine learning techniques applied to
mmWave radar sensor data were investigated.
Sensors 2023,23, 8901 19 of 25
The future of mmWave radar technology seems promising, with plenty of room for
growth and expansion. Here, we present some major trends and developments to keep an
eye on in future years. mmWave radars are projected to play a critical role in advanced
driver-assistance systems (ADASs) and driverless vehicles in automotive applications. In
the future, the increased integration of mmWave radar sensors in automobiles is likely
to improve safety, enable autonomous driving, and improve situational awareness in a
variety of weather conditions. mmWave radars have the potential to transform industrial
applications, such as non-destructive testing, quality control, and process automation.
Future advancements may result in smaller, more adaptable, and cost-effective industrial
mmWave radar systems. There is significant interest in employing mmWave radar for
medical applications, such as the remote monitoring of vital signs, fall detection for the
elderly, and early illness identification in healthcare and medical imaging. Healthcare-
related mmWave radar equipment may advance in the future. mmWave radar systems are
useful in security and surveillance applications, such as perimeter monitoring, intrusion
detection, and surveillance in complex settings. Future advancements could result in more
complex and integrated security solutions. In the field of IoT and smart cities, mmWave
radar sensors could find uses in smart cities for traffic control, environmental monitoring,
and public safety. Space exploration uses mmWave radar technology for remote sensing,
landing, and planetary exploration. As space exploration advances, mmWave radar devices
may play an important part in future space missions. Continued advances in signal
processing methods and the application of machine learning techniques will enhance the
capabilities of mmWave radar systems, allowing for better object detection, tracking, and
imaging. The miniaturization of mmWave radar components and their integration into
smaller and more diversified devices may be future trends, making them more accessible
for a larger range of applications. Challenges include mitigating interference from other
mmWave sources, dealing with atmospheric effects, and ensuring regulatory compliance.
Author Contributions:
Conceptualization, L.R.C.; methodology, L.R.C. and A.S.; software, L.R.C.
and A.S.; validation, L.R.C. and A.S.; formal analysis, L.R.C. and A.S.; investigation, L.R.C. and A.S.;
resources, L.R.C., A.S. and C.K.M.; data curation, L.R.C. and A.S.; writing—original draft preparation,
L.R.C. and A.S.; writing—review and editing, L.R.C., A.S. and C.K.M.; visualization, L.R.C. and
A.S.; supervision, L.R.C. and C.K.M.; project administration, L.R.C.; funding acquisition, L.R.C. All
authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by the INCAPS project: 287918 of the International Partnerships
for Excellent Education, Research and Innovation (INTPART) program from the Research Council of
Norway and the Low-Altitude UAV Communication and Tracking (LUCAT) project: 280835 of the
IKTPLUSS program from the Research Council of Norway.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data sharing not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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