JSS MAHAVIDYAPEETHA

JSS Science and Technology University

“FMCW RADAR-Analysis of working and feature enhancement”

A technical project report submitted in partial fulfillment of the

award of the degree of

BACHELOR OF ENGINEERING

IN

ELECTRONICS AND COMMUNICATION ENGINEERING

BY

Chandan R Sagar 01JST19EC017

Navya Dayanand Naik 01JST19EC050

Under the guidance of

Prof Thyagaraja Murthy A
Assosiate Prefessor
Department of Electronics and Communication Engineering
SJCE, JSS S&TU, Mysuru.

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

2022-2023
 JSS MAHAVIDYAPEETHA

JSS Science and Technology University

CERTIFICATE

This is to certify that the work entitled “FMCW RADAR-Analysis of working

and feature enhancement” is a Bonafide work carried out by Chandan R
Sagar, Navya Dayanand Naik in partial fulfilment of the award of the degree
of Bachelor of Engineering in Electronics and Communication Engineering
for the award of Bachelor of Engineering by JSS Science and Technology
University, Mysuru, during the year 2022-2023. The project report has
been approved as it satisfies the academic requirements in respect to project
work prescribed for the Bachelor of Engineering degree in Electronics and
Communication Engineering.

Under the guidance of Head of the Department

Prof Thyagaraja Murthy A Dr. U.B. Mahadevaswamy

Associate Professor, Professor and Head,
Department of ECE, SJCE, Department of ECE, SJCE,
JSS STU, Mysuru. JSS STU, Mysuru.

Examiners:

1. .........................

2. .........................
 DECLARATION

We do hereby declare that the project titled “FMCW RADAR-Analysis of work-

ing and feature enhancement” is carried out by the project group, under
the guidance of Prof Thyagaraja Murthy A, Associate Professor, Department
of Electronics and Communication Engineering, JSS Science and Technol-
ogy University, Mysuru, in partial fulfilment of requirement for the award of
Bachelor of Engineering by JSS Science and Technology University, Mysore,
during the year 2022-2023.
We also declare that we have not submitted this dissertation to any other
university for the award of any degree or diploma course.

Date:
Place: Mysore

Chandan R Sagar
Navya Dayanand Naik
 ABSTRACT

Overall, the analysis highlights the FMCW radar’s efficient operating prin-
ciple, which combines frequency modulation and the Doppler effect. The
radar’s ability to detect and track pedestrians is greatly enhanced by the
incorporation of feature enhancement techniques like the MTI pulse can-
celler and optimised chirp waveform. The radar is a useful tool in a variety
of real-world situations due to its range, speed measurement accuracy, and
SNR enhancement.The analysis also reveals that the radar can detect tar-
gets with a 1m2 Radar Cross Section (RCS) at a distance of up to 200 metres.
The radar’s transmit power is optimised, and the specified chirp waveform
parameters are used to achieve this range.The FMCW radar exhibits pre-
cise speed measurement capabilities for moving targets, with an accuracy
of 0.18% or lower. The radar’s usefulness in a variety of applications is
further increased by the accuracy that enables accurate speed measure-
ments.Overall, the analysis highlights the FMCW radar’s efficient operating
principle, which combines frequency modulation and the Doppler effect.
The radar’s ability to detect and track pedestrians is greatly enhanced by
the incorporation of feature enhancement techniques like the MTI pulse
canceller and optimised chirp waveform. The radar is a useful tool in a va-
riety of real-world situations due to its range, speed measurement accuracy,
and SNR enhancement.

Keywords : FMCW radar, frequency modulation, Doppler effect, MTI

pulse canceller, optimised chirp waveform, Radar Cross Section (RCS), trans-
mit power, precise speed measurement,accuracy.
Contents

1 Introduction 1
1.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Block Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Literature Review 8
2.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Summary of literature survey . . . . . . . . . . . . . . . . . . . 11

3 Methodology 12
3.1 Range Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Velocity measurement with two chirps . . . . . . . . . . . . . . 16
3.3 Velocity resolution . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Present work carried out 20

4.1 Hardware and Software requirements . . . . . . . . . . . . . . . 20
4.1.1 Software requirements . . . . . . . . . . . . . . . . . . . 20
4.1.2 Hardware requirements . . . . . . . . . . . . . . . . . . . 22
4.2 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5 Results and discussion 34

5.1 Generation of Transmitted and received signal . . . . . . . . . 34
5.2 Generation of beat signal . . . . . . . . . . . . . . . . . . . . . . 35
5.3 1-D FFT output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.4 Velocity,Range and 2-D FFT . . . . . . . . . . . . . . . . . . . . 38

6 Conclusion 41
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.2 Future scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5
List of Figures

1.1 FMCW sawtooth signal model . . . . . . . . . . . . . . . . . . . 3

1.2 FMCW signal 2D FFT processing . . . . . . . . . . . . . . . . . 6
1.4.3 Block diagram for highway scenario . . . . . . . . . . . . . . . 7

3.0.1 Chirp signal, with amplitude as a function of time . . . . . . . 12

3.0.2 Chirp signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.0.3 FMCW radar block diagram . . . . . . . . . . . . . . . . . . . . 13
3.0.4 Multiple IF tones for multiple - object detection . . . . . . . . 14
3.0.5 IF frequency is constant . . . . . . . . . . . . . . . . . . . . . . 14
3.0.6 Multiple IF tones for multiple - object detection . . . . . . . . 15
3.2.1 Two chirp velocity measurement . . . . . . . . . . . . . . . . . 17
3.2.2 Two chirp velocity measurement . . . . . . . . . . . . . . . . . 17
3.2.3 The range-FFT of the reflected chirp frame results in N phasors. 18
3.2.4 Doppler FFT seperates the two objects . . . . . . . . . . . . . . 18

4.1.1 Matlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.2 Arduino IDE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.3 NRF24L01 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.1.4 SPI Read operation . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.5 SPI Write operation . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.6 Arduino UNO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.7 LCD display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.8 Joystick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.1.1 FMCW transmitted and received signal . . . . . . . . . . . . . 34

5.1.2 Enlarged view of transmitted and received signal . . . . . . . . 34
5.2.1 Beat signal in time domain . . . . . . . . . . . . . . . . . . . . 35
5.2.2 Enlarged view of Beat signal in time domain . . . . . . . . . . 35
5.2.3 Beat signal in frequency domain . . . . . . . . . . . . . . . . . 36
5.2.4 Beat signal in frequency domain only lower frequencies . . . . 36
5.3.1 FFT output versus range plot target distance considered 100m 37
5.3.2 FFT output versus range plot target distance considered 150m 37
5.4.1 FFT output vs range plot . . . . . . . . . . . . . . . . . . . . . 38
5.4.2 Velocity vs 2D FFT output plot . . . . . . . . . . . . . . . . . . 38
5.4.3 Range vs 2D FFT output plot . . . . . . . . . . . . . . . . . . . 39
5.4.4 3D plot of 2D velocity and range v/s 2D FFT output . . . . . 39
5.4.5 Transmitter and Receiver . . . . . . . . . . . . . . . . . . . . . 40
5.4.6 Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

6
List of Tables

Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . 7

7
Chapter 1

Introduction

1.1 Preamble
FMCW radar (also known as Frequency-Modulated Continuous Wave radar)
is a special type of radar sensor which radiates continuous transmission
power like a simple continuous wave radar (CW-Radar). Unlike CW radar
FMCW radar can change its operating frequency during the measurement,
that is the transmission signal is modulated in frequency (or in phase).
Possibilities of Radar measurements through runtime measurements are
only technically possible with these changes in the frequency (or phase).
The basic features of FMCW radar are:

• Ability to measure very small ranges to the target (the minimal mea-
sured range is comparable to the transmitted wavelength).

• Ability to measure simultaneously the target range and its relative

velocity.

• Very high accuracy of range measurement

• Signal processing after mixing is performed at a low frequency range,

considerably simplifying the realization of the processing circuits

• Safety from the absence of the pulse radiation with a high peak power.

Frequency Modulated Continuous Wave (FMCW) is a radar technique that

utilizes the principles of frequency modulation to measure the distance, ve-
locity, and other parameters of targets. Unlike traditional pulsed radar sys-
tems, FMCW radar continuously emits a signal with a linearly modulated
frequency. This modulation allows for simultaneous transmission and re-
ception of the radar signal, enabling various applications in fields such as
automotive, aerospace, and industrial sensing. In this introduction, we will
explore the basic principles of FMCW radar and delve into its wide range
of applications. FMCW radar operates by continuously sweeping the trans-
mitted frequency over a defined range. As the signal propagates and en-
counters objects in its path, a portion of the transmitted signal is reflected
back to the radar receiver. The receiver compares the received signal with
the transmitted signal, enabling the detection of changes in frequency. By
analyzing the frequency difference between the transmitted and received
signals, FMCW radar can determine the range or distance to the target.

1
 One of the significant advantages of FMCW radar is its ability to measure
not only the range but also the relative velocity of a target. This is achieved
by tracking the frequency shift of the received signal caused by the Doppler
effect. By continuously measuring the frequency difference, FMCW radar
can determine the speed and direction of moving targets accurately.
The applications of FMCW radar are vast and diverse, owing to its unique
capabilities. One prominent application of FMCW radar is in automotive
sensing, particularly in advanced driver assistance systems (ADAS) and au-
tonomous driving. FMCW radar can provide precise and reliable measure-
ments of the range, velocity, and angle of surrounding objects, enabling
features like adaptive cruise control, collision avoidance, and blind spot de-
tection. It plays a critical role in enhancing road safety and improving the
performance of autonomous vehicles.
In the aerospace industry, FMCW radar finds application in altimeters,
weather radars, and terrain mapping systems. By accurately measuring
the altitude and detecting atmospheric conditions, FMCW radar aids in safe
aircraft navigation, weather monitoring, and hazard avoidance. Industrial
sensing is another domain where FMCW radar excels. Its ability to pen-
etrate various materials and operate in challenging environments makes
it suitable for level sensing, distance measurement, and object detection
in industrial processes. FMCW radar can be employed in inventory man-
agement, liquid level monitoring, and collision avoidance systems in ware-
houses, manufacturing plants, and construction sites.
Moreover, FMCW radar has applications in maritime navigation, en-
vironmental monitoring, and even healthcare. In marine applications, it
assists in ship collision avoidance and aids navigation in adverse weather
conditions. In environmental monitoring, FMCW radar can measure rain-
fall, snow depth, and soil moisture. In healthcare, it can be used for non-
invasive vital sign monitoring and gesture recognition.In conclusion, FMCW
radar offers a powerful and versatile sensing technique with numerous ap-
plications across various industries. Its ability to provide accurate range,
velocity, and other parameter measurements makes it an indispensable tool
for automotive, aerospace, industrial, and many other fields. With ongoing
advancements in technology, FMCW radar continues to evolve, unlocking
new possibilities for sensing, automation, and improving our understand-
ing of the world around us.
FMCW stands for Frequency Modulated Continuous Waves. This radar
basically measures the range, velocity, and angle of arrival of objects in
front of it.There are several different modulations that are used in FMCW
signals such as sawtooth, triangle and sinusoidal. In our case, we will con-
sider a sawtooth model of the FMCW signal. As it can be seen, transmitted
frequency increases linearly as a function oftime during Sweep Repetition
Period or Sweep Time (T). Starting frequency is fc ,Frequency at any given
time t can be found by:

B
f (t) = fc + t (1.1.1)
T
Here, B
T is a chirp rate and can be thought as a “speed” of the frequency
change. We can substitute it with α:

B
α= (1.1.2)
T

2
 Figure 1.1: FMCW sawtooth signal model

By using frequency change over time, we can find the instantaneous phase:
Z t
αt2
µ(t) = 2π f (t) dt + µ0 = 2π(fc t + ) + ϕ0 (1.1.3)
0 2

Therefore, the transmitted signal in the first sweep, considering ϕ0 to be

the initial phase of the signal, can be written as: The equation above only
describes the transmitted signal in the first sweep.If we want to describe
the transmitted signal in the nth sweep, a modification should be made. We
can consider ts as a time from the start of nth sweep and define ts as:

t = nT + ts where 0 < ts < T (1.1.4)

Therefore, our signal form for the transmitted signal in the nth sweep be-
comes:
αt2s
xtx (t) = A(cos(µ(t)) = A cos(2π(fc (nT + ts ) + ) + ϕ0 ) (1.1.5)
2
Let’s consider an object located at an initial distance of R which is moving
with a relative velocity of v. The returned signal from the object will have
the same form, but with some delay τ which can be defined as:

2(R + vt) 2(R + v(nT + ts ))

τ= = (1.1.6)
c c
Considering the delay τ , we can describe the returned signal as:

α(ts − τ )2 )
xrx (t) = B cos(µ(t − τ )) = B cos(2π(fc (nT + ts − τ ) + + ϕ0 ) (1.1.7)
2
According to the FMCW radar principle, the returned signal is mixed with
the transmitted signal:
xm (t) = xtx (t)xrx (t) (1.1.8)
The equation above will include cosine multiplication which can be trans-
formed using the trigonometric formula below:

cos(α) cos(β) = (cos(α + β) cos(α − β))/2 (1.1.9)

3
The sum term in our case will have a very high frequency which will be
filtered out. Therefore, the resulting signal will only include the subtraction
term:
AB αt2 α(ts − τ )2 )
xm (t) = cos(2πfc (nT + ts ) + s − fc (nT + ts − τ ) − (1.1.10)
2 2 2
After simplification we get:

AB ατ 2
xm (t) = cos(2π(fc τ + ατ ts − )) (1.1.11)
2 2
If we replace τ with its equivalent from Equation 6.6 , we will get:

AB 2(R + v(nT + ts )) 2(R + v(nT + ts ))

xm (t) = cos(2π(fc + αts
2 c c (1.1.12)
4(R + v(nT + ts ))2
−α ))
2c2
We can simplify and write the equation as:

AB 2αR 2fc v 2αvnT 4αRv 4αnT v 2

xm (t) = cos(2π(( + + − − )ts
2 c c c c2 c2 (1.1.13)
2fc v 4αRv 2fc R 2αvt2s 2αR2 2αv 2 n2 T 2 2αv 2 t2s
+( − )nT + + − − − ))
c c2 c c c2 c2 c2
If we look at the Equation 6.13 , we see that there is a frequency and a
phase that influences how the signal changes over time. In the literature,
the frequency is usually named as a ”beat frequency”. The difference in
frequency between the transmitted and the received signals is denoted by
fb in the Figure 6.1. The above equation shows that the ”beat frequency”
is affected by number of terms such as initial range to the object, object’s
velocity and the chirp number. Few observations can be made based equa-
v2
tion first, we see that the values of the expressions 4αRc
c2
and 4αnT
c2
re very
2fc v
small and can easily be neglected. Apart from that, the terms c and αvnT c
are relatively small and their effect to the main frequency component 2αR c
can be considered negligible. Second, other terms which have c2 2 in their
denominators are also very small and can be neglected too. Third, the term
2
with t2s , 2αvt
c
s
is also very small (0.0024) and can be neglected as well. Con-
sequently, xm (t) equation can be approximated as:

AB 2αR 2fc vn 4πfc R

xm (ts , n) = cos(2π( ts + T) + ) (1.1.14)
2 c c c

where the term 4πfcc R is a constant phase term, since R is an initial distance
at which the object is located.
The frequency spectrum of the signal computed over one modulation period
will give us 2αR
c as a main frequency component which is the beat frequency.
The derivation of the beat frequency is usually based on the Fast Fourier
Transform (FFT) algorithm which efficiently computes the Discrete Fourier
Transform (DFT) of the digital sequence. Consequently, by applying the FFT
algorithm over one signal period, we can easily find the beat frequency and

4
thus the range to the target:

2αR fb c
fb = and R = (1.1.15)
c 2α
Range resolution of a radar is the minimum range that the radar can dis-
tinguish two targets on the same bearing. Based on the above equation
and substituting α with Equation 6.2, we can find the range resolution of
a radar. It is based on the fact that the frequency resolution ∆fb of the
mixed signal is bounded by the chirp frequency which means that in order
to be able to detect two different objects, the frequency difference of the
mixed signal returned from that objects cannot be smaller than the chirp
frequency. This intuition gives the range resolution which can be found as:

2B∆R 1 c
∆fb = . and ∆R = (1.1.16)
c T 2B

On the other hand, there is also a phase 2fcc v .nT associated with the beat
frequency which changes linearly with the number of sweeps. The change
of the phase indicates how the frequency of the signal changes over conse-
quent number of periods. This change is based on the Doppler frequency
shift which is the shift in frequency that appears as a result of the relative
motion of two objects. The Doppler shift can be used to find the velocity of
the moving object:
2fc v fd c
fd = and v = (1.1.17)
c 2fc
The Doppler shift of the signal can be found by looking at the frequency
spectrum of the signal over n consecutive periods (n·T). In this case, the
FFT algorithm is applied on the outputs of the first FFT. Figure 6.2 de-
scribes this process; first, the row-wise FFT is taken on the time samples,
second, the column-wise FFT is taken on the output of the first FFT. After
two dimensional FFT processing, we have a range-Doppler map which con-
tains range and velocity information of the target.
Velocity resolution of a radar is the minimum velocity difference between
two targets travelling at the same range of which the radar can distin-
guish. It can be found in a similar way as the range resolution. Here,
the Doppler frequency change over n chirp durations is bounded by the
frequency resolution(∆fd ≥ nT 1
). Thus, the velocity resolution can be ex-
pressed as:
c 1
∆v = . (1.1.18)
2fc nT

5
 Figure 1.2: FMCW signal 2D FFT processing

1.2 Problem Statement

In current situation, driver driving a vehicle acts accordingly based on the
visuals on other vehicles moving ahead and behind him.The driver can’t
precisely estimate the velocity or distance of those obstacle/vehicles. There
is a need for a device or system which can estimate the accurate velocity and
distance of those vehicles, which helps the driver to make decisions based
on the continuous results shown by the system.

1.3 Motivation
This topic touches a lot of topics like RF circuit design, Microwaves and
Antennas, Analog circuits, Advanced Communication Engineering,Thermal
imaging which are very exciting to deal with. Along with these the outcome
of this project help the user such that it has the potential to identify the
object.

1.4 Block Diagram

6
 Figure 1.4.3: Block diagram for highway scenario

1.5 Objectives
• Generating samples of FMCW chirp signal in matlab.

• Simulation of the highway scenario where the vechicles distance and

velocity have to be detected.

• Simulation of the forest scenario where to establish an effective com-

munication for forest officers in dense vegetation and uneven terrain.

• Writing a program to evaluate the above scenarios.

7
Chapter 2

Literature Review

To explore and analyze existing scholarly works to gain a comprehensive

understanding of the current state of knowledge, identify research gaps,
evaluate methodologies, avoid duplication of effort, develop a conceptual
framework, and support the research methodology the literature survey has
been carried out for the project work where several authors and their con-
tributions have been analyzed.

2.1 Previous Research

1. Assembly of an S-band FMCW Doppler Radar System for improved
Pedestrians Detection
A Frequency Modulated Continuous Wave (FMCW) Doppler radar was
assembled to detect passing-by pedestrians. Its operating frequency is
at 2.4GHz with transmit power of 10.41dBm. Range resolution of the
radar is 2.8meters at 53.2MHz signal bandwidth and chirp waveform
of 40ms. The radar exploits Doppler principle to acquire the range
and velocity information of targets whilst a Moving Target Indicator
(MTI) pulse canceller is utilized to filter incoming noise signal. With
the use of Chirp period-bandwidth product of Frequency Modulated
(FM) waveform and de-ramping process, the radars’ Signal to Noise
Ratio (SNR) was improved up to 42dB. The attained maximum range
is about 200meters for target with Radar Cross Section (RCS) of 1m2
. The constructed radar is capable to measure speed of moving target
at 0.645m/s and above with great accuracy. The radar can detect and
determine position of pedestrians with 0.18% percentage error.

2. Design of an FMCW radar baseband signal processing system for au-

tomotive application
For a typical FMCW automotive radar system, a new design of base-
band signal pro cessing architecture and algorithms is proposed to
overcome the ghost targets and overlapping problems in the multi-
target detection scenario. To satisfy the short measurement time con-
straint without increasing the RF front-end loading, a three-segment
waveform with different slopes is utilized. By introducing a new pair-
ing mechanism and a spatial filter design algorithm, the proposed de-
tection architecture not only provides high accuracy and reliability,
but also requires low pairing time and computational loading. This

8
 proposed baseband signal processing architecture and algorithms bal-
ance the performance and complexity, and are suitable to be imple-
mented in a real automotive radar system. Field measurement re-
sults demonstrate that the proposed automotive radar signal process-
ing system can perform well in a realistic application scenario.

3. Design of FMCW Radars for Active Safety Applications

With 28,000 road fatalities in 2012 in Europe, car manufacturers, au-
tomotive electronics suppliers, and universities are working to develop
new electronic systems for accident prevention and collision mitiga-
tion. In the near future, vehicle-to-vehicle and vehicle-to-infrastructure
networks based on dedicated short-range communication (DSRC) tech-
nology will warn drivers when dangerous conditions are detected. In-
formation collected by radars, cameras, and other sensors integrated
in vehicles and road infrastructure will be used to determine the driv-
ing situation and warn drivers of potentially dangerous events.

4. Obstacle Avoidance onboard MAVs using a FMCW RADAR

Micro Air Vehicles (MAVs) are increasingly being used for complex
or hazardous tasks in enclosed and cluttered environments such as
surveillance or search and rescue. With this comes the necessity
for sensors that can operate in poor visibility conditions to facilitate
with navigation and avoidance of objects or people. Radar sensors in
particular can provide more robust sensing of the environment when
traditional sensors such as cameras fail in the presence of dust, fog
or smoke. While extensively used in autonomous driving, miniature
FMCW radars on MAVs have been relatively unexplored. This study
aims to investigate to what extent this sensor is of use in these environ-
ments by employing traditional signal processing such as multi-target
tracking and velocity obstacles. The viability of the solution is evalu-
ated with an implementation on board a MAV by running trial tests in
an indoor environment containing obstacles and by comparison with
a human pilot, demonstrating the potential for the sensor to provide
a more robust sense and avoid function in fully autonomous MAVs.

5. Digital Signal Processing for Frequency Modulated Continuous Wave

RADARs.
RADAR is an established technology, but interest has been stimulated
recently by demands of driver assistance systems and emerging self-
driving cars for applications including proximity warning, blind spot
detection, adaptive cruise control, and emergency braking. Advanced
driver-assistance systems (ADAS) and autonomous driving (AD) sys-
tems typically combine several types of sensors, such as cameras,
RADAR’s, and LiDAR’s. Different types of sensors have their strengths,
and effectively complement each other. Cameras and the appropriate
machine vision algorithms can “see” lane marking and recognize traf-
fic signals and signs. LiDAR can offer high (cm-level) resolution and
high density of collected data points. RADAR technology is indispens-
able in ADAS applications because of its robustness to a variety of
environmental conditions, like rain, fog, snow, and its ability to di-
rectly and precisely measure range and velocity. Basic use cases can
rely on RADAR sensors only, enabling cost-effective solutions. RADAR

9
 can be used to augment, cross-check or ‘fuse’ situational models de-
rived using advanced computer vision algorithms.

6. A Study of an X-band FMCW Radar for Small Object Detection

This paper presents the design of the lowcost x-band frequency mod-
ulated continuous wave (FMCW) radar for small object detection. The
FMCW radar is implemented at the frequency range of 10 GHz (X-
band). The horn antenna is designed to construct the array antenna
with an antenna gain of 23 dBi. The radar targets are composed of
a square wooden post, circular cross-section metal rod and square
metal plate. The experiment will be conducted in an anechoic cham-
ber. The experiment results of the three radar targets are illustrated.
Also, the moving detection of a square metal plate will be performed.
The result showed that the proposed system is efficiently employed to
detect small objects.

7. A Scanning FMCW-Radar System for the Detection of Fast Moving Ob-

jects
This paper describes a mobile broadband 57 GHz – 64 GHz FMCW
radar sensor system designed to improve the situational awareness
of soldiers on peace-making or peacekeeping missions by detecting
projectiles in flight. In order to obtain an instantaneous coverage of
the azimuthal field of view, a frequency-scanning meander antenna
is used. Thereby, the sensors are provisioned to employ a scanning
principle by radiating broadband waveforms with different frequen-
cies into separate spatial directions

8. Animal Detection Using Thermal Images and Its Required Observation

Conditions
Information about changes in the population sizes of wild animals is
extremely important for conservation and management. Wild animal
populations have been estimated using statistical methods, but it is
difficult to apply such methods to large areas. Support systems for
the automated detection of wild animals is developed by using remote
sensing images. The computer-aided detection of moving wild animals
(DWA) algorithm is used to thermal remote sensing images. The DWA
algorithm is useful for thermal images and to clarify the optimal con-
ditions for obtaining thermal images. A method is developed based
on the algorithm to extract moving wild animals from thermal remote
sensing images. This means that the proposed method can reduce
the person-hours required to survey moving wild animals from large
numbers of thermal remote sensing images.

9. The temperature measurement technology of infrared thermal imag-

ing and its applications review
This paper summarized the application of infrared thermal imager re-
search, and prospects the research direction in the field of it related.
This paper briefly introduces the principle of infrared thermal imaging
temperature measurement technology and the modern infrared ther-
mal imager performance parameters, and the infrared thermal imager
in the related field application examples at home and abroad in recent
years.

10
 10. Thermal Sensor-Based Multiple Object Tracking for Intelligent Live-
stock Breeding
This paper proposes the method of tracking animals using a single
thermal sensor. The key idea of the proposed method is to represent
the foreground (i.e., animals) easily obtained by a simple threshold-
ing in a thermal frame as a topographic surface, which is very helpful
for finding the boundary of each object even in cases with overlap-
ping. Based on the segmentation results derived from morphological
operations on the topographic surface, the center positions of all the
animals are consistently updated with an efficient refinement scheme
that is robust to the abrupt motions of animals.

11. Image Processing based application of Thermal Imaging for Monitor-

ing Stress Detection in Tomato Plants
This paper presents a comprehensive study of the application of ther-
mal imaging in the field of agriculture and stress detection in plants.
Improved method is designed for the process of stress detection in
plants by using a combination of both numeric values obtained in the
form of temperature measures as well as the thermal images obtained
that is using multimodal analysis. Experiment is performed by choos-
ing tomato plants as our study specimen and using a thermal camera
for taking thermal images along with the temperature readings.

2.2 Summary of literature survey

Having reviewed multiple research papers from various domains The sum-
mary describes two different radar systems: a Frequency Modulated Con-
tinuous Wave (FMCW) Doppler radar for pedestrian detection and a base-
band signal processing architecture for a car radar system. It employs a
Moving Target Indicator (MTI) pulse canceller to filter out noise signals,
thereby enhancing the Signal to Noise Ratio (SNR). The SNR is further im-
proved by using a chirp period-bandwidth product and a de-ramping tech-
nique, boosting it by up to 42dB. With these features, the radar achieves a
maximum range of approximately 200 meters for targets with a 1m2 Radar
Cross Section (RCS). It exhibits precise speed measurement capabilities for
moving targets with an accuracy of 0.18% and can accurately identify and
locate pedestrians.
The difficulties of multi-target detection in an automobile radar system
are addressed by the baseband signal processing architecture outlined in
the second section of the summary. In order to support brief measurement
durations without taxing the RF front-end, a novel three-segment waveform
with a range of slopes is introduced. The suggested architecture consists
of a novel pairing approach and an algorithm for creating spatial filters,
which increase target identification reliability and accuracy while lowering
pairing time and computational load. The proposed system provides a bal-
ance between performance and complexity, making it appropriate for use in
practical automobile radar applications. The suggested vehicle radar signal
processing system operates effectively in realistic scenarios, according to
field measurement data.

11
Chapter 3

Methodology

The fundamental concept in radar systems involves the transmission of an

electromagnetic signal that interacts with objects in its path. In the case
of Frequency Modulated Continuous Wave (FMCW) radars, the transmit-
ted signal is a chirp waveform, where the frequency increases linearly with
time. A chirp signal is characterized by its slope, which is denoted as S

Figure 3.0.1: Chirp signal, with amplitude as a function of time

and represents the rate of change of frequency over time. The slope of the
chirp determines the range resolution of the radar system. A larger slope
corresponds to a higher range resolution, allowing the radar to distinguish
between closely spaced objects. Conversely, a smaller slope provides lower
range resolution but allows for longer maximum detection range.
By analyzing the reflected or ”echo” signal, the radar system can deter-
mine the time delay and frequency shift of the received signal, which are
used to calculate the range and velocity of detected objects. This process
is known as ”range-Doppler processing” and is a key technique in FMCW
radar systems.
In FMCW radar systems, a chirp signal is generated by a synthesizer
(synth) and transmitted through a transmit antenna (TX ant). As the chirp
signal encounters objects in its path, it reflects back and is captured by a
receive antenna (RX ant). To combine the received and transmitted signals,
a frequency mixer is utilized. The purpose of the mixer is to create an inter-
mediate frequency (IF) signal by merging the two input signals. A frequency

12
 Figure 3.0.2: Chirp signal

Figure 3.0.3: FMCW radar block diagram

mixer, an electronic component, takes two sinusoidal inputs represented by

equations 3.0.1 and 3.0.2

x1 = sin(ω1 ∗ t + ϕ1 ) (3.0.1)

x2 = sin(ω2 ∗ t + ϕ2 ) (3.0.2)
The output signal, xout , is generated with a new frequency that corresponds
to the difference between the instantaneous frequencies of the input signals
represented by equation

xout = sin[(ω1 − ω2 ) ∗ t + (ϕ1 − ϕ2 )] (3.0.3)

The phase of the output signal is determined by the difference in phases of

the input signals.
To visualize the operation of the frequency mixer, one can examine the
frequency representation of the transmitted and received chirp signals over
time. By subtracting the frequency components of the transmitted chirp
from the received chirp, the mixer produces the IF signal. Notably, the
distance between the two frequency lines remains constant, indicating that
the IF signal consists of a tone with a fixed frequency. The relationship
between the time delay (t) and the distance (d) to the detected object can
be mathematically expressed as in equation 3.0.4. This relationship allows
for the determination of the distance based on the constant frequency tone
present in the IF signal.
2d
τ= (3.0.4)
c
where c represents the speed of light

13
 Figure 3.0.4: Multiple IF tones for multiple - object detection

Figure 3.0.5: IF frequency is constant

In summary, an FMCW radar system utilizes a frequency mixer to com-

bine the transmitted and received chirp signals, generating an IF signal with
a fixed frequency tone. By analyzing this signal, the radar system can de-
termine the distance to detected objects based on the relationship between
time delay and distance.The initial phase of the IF signal, denoted as ϕ0 ,
represents the phase difference between the TX chirp and the RX chirp at
the beginning of the IF signal. This phase can be calculated using equation
3.0.5. Mathematically, it can be further simplified to equation 3.0.6

ϕ0 = 2πf cτ (3.0.5)

Where τ is round trip delay,

4πd
ϕ0 = (3.0.6)
λ
where d is the distance to the object and lambda represents the wavelength.
In summary, for an object located at a distance d from the radar, the IF

14
signal will be a sine wave described by equation 3.0.7:

A sin(2πf0 + ϕ0 ) (3.0.7)

Here, f0 = S2d
c , where S represents the slope of the chirp and c is the speed of
light. The phase ϕ0 is given by 4πd/λ. Up until now, we have assumed that
the radar has detected only one object. However, let’s consider a scenario
where multiple objects are detected. In such cases, the received RX chirps
from different objects will exhibit varying time delays proportional to their
respective distances. Consequently, these different RX chirps will translate
into multiple IF tones, each having a constant frequency
To separate and distinguish the various tones within the IF signal compris-
ing multiple objects, Fourier transform processing is employed. By applying
Fourier transform processing, a frequency spectrum is obtained, where sep-
arate peaks indicate the presence of different tones, each corresponding to
an object at a specific distance. The IF signal consisting of multiple tones,

Figure 3.0.6: Multiple IF tones for multiple - object detection

it is necessary to apply a Fourier transform. This transforms the signal into

a frequency spectrum, which displays distinct peaks corresponding to the
different tones. Each peak represents the presence of an object at a spe-
cific distance. In essence, the Fourier transform separates and identifies
the individual tones within the IF signal, enabling the detection and char-
acterization of objects based on their respective distances.

3.1 Range Resolution

Range resolution refers to the capability of a radar system to differentiate
between multiple objects. As objects move closer together, there comes a
point where the radar system cannot distinguish them as separate entities.
According to Fourier transform theory, one can enhance the resolution by
extending the length of the IF (Intermediate Frequency) signal. In other
words, by increasing the duration of the IF signal, the radar system be-
comes more capable of discerning closely spaced objects as distinct targets.
To achieve a longer IF signal, it is necessary to proportionally increase the
bandwidth. When the length of the IF signal is increased, the resulting IF

15
spectrum displays two distinct peaks. According to Fourier transform the-
ory, the ability to resolve frequency components depends on the observation
window (T). It states that frequency components can be distinguished if their
frequency difference is greater than 1/T Hz. This relationship is mathemat-
ically expressed as in equation 3.0.8. In simpler terms, two tones within the
IF signal can be resolved in terms of frequency as long as the difference in
their frequencies exceeds the reciprocal of the observation window multi-
plied by the speed of light (c).
1
∆f > (3.1.1)
Tc
where Tc is the observation interval. Considering the relationship ∆f =
S2∆d/c, Equation 8 can be rewritten as ∆d > c/(2ST c) = c/2B (since B = TSc ).
This implies that the minimum resolvable distance (∆d) is greater than the
reciprocal of half the chirp bandwidth 2B
c
). The range resolution (dRes ) of an
FMCW radar system depends solely on the bandwidth swept by the chirp,
as indicated in Equation 3.0.9:
c
dRes = (3.1.2)
2B
Therefore, an FMCW radar with a chirp bandwidth spanning a few GHz will
achieve a range resolution on the order of centimeters. For example, a chirp
bandwidth of 4 GHz corresponds to a range resolution of approximately 3.75
cm.

3.2 Velocity measurement with two chirps

To measure the velocity of an object, an FMCW radar system transmits two
chirps with a time separation of Tc . Each reflected chirp is processed using
a Fast Fourier Transform (FFT) to determine the object’s range (range-FFT).
The range-FFT results for both chirps will have peaks at the same loca-
tions, but with different phases. The phase difference between the peaks
corresponds to the motion of the object over the duration of Tc and can be
calculated using Equation 3.0.10:

4πvTc
∆ϕ = (3.2.1)
λ
Using Equation 3.0.11, the velocity (v) can be derived as:

λ∆ϕ
v= (3.2.2)
4πTc
Since the velocity measurement is based on a phase difference, there
is an inherent ambiguity. The measurement is unambiguous only if the
absolute value of the phase difference ∆ϕ is less than π. From Equation
3.0.11, it can be mathematically derived that the velocity (v) should be less
than 4πT λ
c
to avoid ambiguity.
Furthermore, Equation 3.0.12 provides the maximum relative speed
(vmax ) that can be measured by two chirps separated by Tc. A higher vmax re-
quires reducing the transmission time between chirps. It can be expressed
as:

16
 Figure 3.2.1: Two chirp velocity measurement

λ
vmax = (3.2.3)
4Tc
In simpler terms, the maximum relative speed that can be accurately
measured using two chirps with a time separation of Tc is determined by
the wavelength (lambda) divided by four times the chirp duration (Tc )
The velocity measurement method using two chirps becomes ineffective
when multiple moving objects with different velocities are located at the
same distance from the radar. Since these objects are equidistant, they
will produce reflected chirps with identical intermediate frequency (IF) fre-
quencies. As a result, the range-FFT processing will yield a single peak,
representing the combined signal from all these equidistant objects. A sim-
ple phase comparison technique cannot distinguish between them.
To overcome this challenge and measure the velocities accurately, the
radar system needs to transmit more than two chirps. It utilizes a set of N
equally spaced chirps known as a chirp frame. Figure 3.0.7 illustrates the
frequency variation over time for a chirp frame.

Figure 3.2.2: Two chirp velocity measurement

The processing technique involves the following steps, considering the

17
example of two objects equidistant from the radar but with different ve-
locities, v1 and v2. The range-FFT is applied to the reflected chirp frame,
resulting in N peaks located at the same positions, but each peak having
a different phase that incorporates the phase contributions from both ob-
jects (represented by the red and blue phasors in Figure 3.0.8). To resolve

Figure 3.2.3: The range-FFT of the reflected chirp frame results in N

phasors.

the velocities of the individual objects, a second FFT called the Doppler-FFT
is performed on the N phasors obtained. This Doppler-FFT helps separate
the contributions of the two objects, as shown in Figure 3.0.9. The phase

Figure 3.2.4: Doppler FFT seperates the two objects

differences between consecutive chirps for the respective objects are repre-
sented by ω1 and ω2. Using Equation 3.0.13, the velocities v1 and v2 can
be calculated as:
λω1 λω2
v1 = , v2 = (3.2.4)
4πT c 4πT c
These equations relate the phase differences (ω1and ω2) to the velocities (v1
and v2 ) of the objects, taking into account the wavelength (λ) and the chirp
duration (Tc ). By analyzing the results of the Doppler-FFT, the radar system
can determine the velocities of the individual objects accurately.

3.3 Velocity resolution

According to the theory of discrete Fourier transforms, two discrete fre-
quencies, ω1 and ω1, can be distinguished if the frequency difference, ∆ω =
ω2 − ω1 , is greater than 2π/N radians per sample. Using Equation 3.0.10,
which states∆ω = λ4 , we can mathematically determine the velocity resolu-
tion (vres ) when the frame period, Tf = N Tc (as given in Equation 3.0.14):

λ
v > vres = (3.3.1)
2T f

18
This equation demonstrates that the velocity resolution of the radar system
is inversely proportional to the frame time (Tf ). In other words, a longer
frame time allows for better velocity resolution, enabling the radar to dis-
tinguish between objects with smaller velocity differences.

19
Chapter 4

Present work carried out

4.1 Hardware and Software requirements

4.1.1 Software requirements
MATLAB
MATLAB, short for ”Matrix Laboratory,” is a powerful programming and nu-
merical computing environment widely used in various fields such as en-
gineering, mathematics, physics, finance, and data analysis. It provides a
comprehensive set of tools and functions that facilitate data analysis, vi-
sualization, algorithm development, and simulation.At its core, MATLAB
excels in handling matrices and arrays, making it well-suited for mathe-
matical computations and linear algebra operations. It offers an extensive
library of built-in functions, encompassing mathematical operations, statis-
tical analysis, signal processing, image processing, optimization algorithms,
and more. This vast collection of functions enables users to perform com-
plex calculations and manipulate data efficiently.
MATLAB provides a user-friendly and interactive environment for pro-
gramming. Its integrated development environment (IDE) features a com-
mand window where users can enter commands and execute them immedi-
ately. This interactive mode allows for rapid prototyping and iterative devel-
opment. Additionally, MATLAB supports script and function files, enabling
the creation of more extensive and structured programs.
The visualization capabilities of MATLAB are noteworthy. It offers a wide
range of built-in plotting and graphing functions, allowing users to create
2D and 3D plots, histograms, scatter plots, and other visual representations
of data. These visualizations are highly customizable, providing options
for labels, titles, colors, and other formatting attributes.MATLAB supports
the import and export of data from various file formats, including spread-
sheets, text files, images, and sound files. This flexibility enables seamless
integration with external data sources and facilitates data preprocessing
and analysis. Furthermore, MATLAB supports the creation of graphical
user interfaces (GUIs) using its GUIDE (Graphical User Interface Develop-
ment Environment) tool. This feature empowers users to build interactive
applications with buttons, menus, sliders, and other controls, providing
a more user-friendly experience for their programs. MATLAB’s extensive
functionality is complemented by its compatibility with other programming
languages. It supports integration with C/C++, Python, Java, and other

20
 Figure 4.1.1: Matlab

languages, enabling users to leverage existing code libraries and collabo-

rate with colleagues who use different programming environments.

Arduino IDE
The Arduino Integrated Development Environment (IDE) is a software plat-
form designed to simplify the development process for Arduino microcontroller-
based projects. It provides a user-friendly interface and a set of tools that
enable programmers, hobbyists, and professionals to write, compile, and
upload code to Arduino boards easily.The Arduino IDE offers a streamlined
programming experience, especially for beginners, by providing a simplified
coding environment. It utilizes a variant of the C++ programming language,
making it accessible to those familiar with basic programming concepts.
The IDE includes a text editor with syntax highlighting and auto-completion
features, which help in writing code accurately and efficiently.
In addition to the code editor, the Arduino IDE incorporates a built-in
compiler that translates the code into machine-readable instructions for
the Arduino board. It supports a wide range of Arduino board models and
configurations, allowing developers to select the appropriate board and port
settings for their project. The IDE also provides a comprehensive library of
pre-written functions and examples that can be easily referenced and uti-
lized, saving time and effort in coding complex functionalities.One of the
notable features of the Arduino IDE is the Serial Monitor. This tool enables
bidirectional communication between the Arduino board and the computer,
allowing developers to send and receive data for debugging and testing pur-
poses. The Serial Monitor also provides a convenient way to monitor sensor
readings, debug code, and display output messages during runtime.
The Arduino IDE further simplifies the uploading process, allowing users
to effortlessly transfer their compiled code to the Arduino board. It supports
various connection methods, such as USB, Bluetooth, or Wi-Fi, depending
on the board’s capabilities. Once the code is successfully uploaded, the Ar-
duino board executes the instructions, enabling the project to function as
intended.Furthermore, the Arduino IDE is an open-source platform, mean-
ing its source code is freely available for modification and improvement.
This openness fosters an active and supportive community of developers
who contribute libraries, tutorials, and extensions, expanding the capabil-
ities of the IDE and enhancing the overall Arduino ecosystem.

21
 Figure 4.1.2: Arduino IDE

4.1.2 Hardware requirements

NRF24L01

Figure 4.1.3: NRF24L01

The nRF24L01 is a single-chip 2.4GHz transceiver with an embedded

baseband protocol engine (Enhanced ShockBurst), designed for ultra low
power wireless applications.The nRF24L01 is designed for operation in the
worldwide ISM frequency band at 2.400 - 2.4835GHz.An MCU (microcon-
troller) and very few external passive components are needed to design a
radio system with the nRF24L01.The nRF24L01 is configured and operated
through a Serial Peripheral Interface (SPI.) Through this interface the reg-
ister map is available. The register map contains all configuration registers
in the nRF24L01 and is accessible in all operation modes of the chip.
The NRF24L01 is a versatile wireless transceiver module designed for short-
range communication in the 2.4 GHz frequency band. It offers a reliable and
cost-effective solution for wireless data transmission between microcon-
trollers or other devices. With a communication range of up to 100 meters
in open spaces, it is well-suited for projects requiring wireless connectivity

22
within a limited area. The module supports 126 RF channels, allowing mul-
tiple NRF24L01 modules to coexist without interference. It offers selectable
data rates of 250 kbps, 1 Mbps, and 2 Mbps, enabling you to balance speed,
range, and power consumption according to your specific application needs.
The NRF24L01 communicates with microcontrollers through an SPI inter-
face, facilitating easy integration with popular development platforms like
Arduino or Raspberry Pi. Operating at a power supply range of 1.9V to 3.6V,
it consumes low power, making it suitable for battery-powered applications.
With its features and capabilities, the NRF24L01 module is widely used in
wireless projects such as remote control systems, wireless sensor networks,
home automation, and more.

Features
1. True single chip GFSK transceiver

2. Complete OSI Link Layer in hardware

3. Enhanced ShockBurst™

4. Auto ACK and retransmit

5. Address and CRC computation

6. On the air data rate 1 or 2Mbps

7. Digital interface (SPI) speed 0-8 Mbps

8. 125 RF channel operation

9. Short switching time enable frequency hopping

10. Fully RF compatible with nRF24XX

11. 5V tolerant signal input pads

12. 20-pin package (QFN20 4x4mm)

13. Uses ultra low cost +/- 60 ppm crystal

14. Uses low cost chip inductors and 2-layer PCB

15. Power supply range: 1.9 to 3.6 V

DEVICE CONFIGURATION
All configuration of nRF24L01 is defined by values in some configuration
registers. All these registers are writable via the SPI interface. The SPI
interface is a standard SPI interface with a maximum data rate of 10Mbps.
Most registers are readable.

23
SPI Timing
The interface supports SPI. SPI operation and timing is given in Figure to
Figure and in Table and Table . The device must be in one of the standby
modes or power down mode before writing to the configuration registers. In
Figure to Figure the following notations are used:
Cn - SPI Instruction Bit
Sn -Status Register Bit
Dn - Data Bit (note: LSByte to MSByte, MSBit in each byte first)

Figure 4.1.4: SPI Read operation

Figure 4.1.5: SPI Write operation

Telemetry
The nRF24L01 is a 2.4 GHz transceiver module that operates in the ISM (In-
dustrial, Scientific, and Medical) band. It offers features like low power con-
sumption, high data rate, and a range of up to 100 meters in open spaces.
The module supports point-to-point and multi-point communication. To
establish communication between two nRF24L01 modules, the following
hardware setup is required:
1. Two nRF24L01 modules

2. Microcontrollers (Arduino, Raspberry Pi, etc.) for controlling the mod-

ules

3. Power supply for the modules and microcontrollers

4. Connecting wires

Pin Connections
The nRF24L01 module has eight pins that need to be connected to the mi-
crocontroller. The following pin connections are necessary:
• VCC: Connect to the 3.3V power supply.

• GND: Connect to the ground.

• CE (Chip Enable): Connect to a digital pin on the microcontroller.

24
 • CSN (Chip Select Not): Connect to a digital pin on the microcontroller.

• SCK (Serial Clock): Connect to the SPI clock pin on the microcon-
troller.

• MOSI (Master Out Slave In): Connect to the SPI data output pin on
the microcontroller.

• MISO (Master In Slave Out): Connect to the SPI data input pin on the
microcontroller.

• IRQ (Interrupt Request): Optional pin for interrupt-driven communi-

cation.

Communication Protocol:
The nRF24L01 modules use a protocol that consists of a transmitter (TX)
and a receiver (RX). The transmitter sends data packets, and the receiver
receives and decodes them. The modules operate on a set of pre-defined
channels and share a common address. To enable communication between
two nRF24L01 modules, the following steps are involved:
• Configure the module settings (channel, data rate, power level, etc.).

• Set the address for each module (to distinguish multiple modules in
the same environment).

• Initialize the necessary libraries or drivers for the microcontroller.

• Implement the necessary functions to send and receive data using the
nRF24L01 modules.

• Handle potential errors and exceptions during communication.

Data transmission using the nRF24L01 modules typically involves the fol-
lowing steps:
• Prepare the data to be transmitted on the transmitter side.

• Send the data packet from the transmitter.

• Receive the data packet on the receiver side.

• Extract the received data from the packet.

Arduino UNO
The Arduino Uno is one kind of microcontroller board based on ATmega328,
and Uno is an Italian term which means one. Arduino Uno is named for
marking the upcoming release of microcontroller board namely Arduino
Uno Board 1.0. This board includes digital I/O pins-14, a power jack, ana-
log i/ps-6, ceramic resonator-A16 MHz, a USB connection, an RST button,
and an ICSP header. All these can support the microcontroller for further
operation by connecting this board to the computer. The power supply of
this board can be done with the help of an AC to DC adapter, a USB cable,
otherwise a battery.
The Arduino Uno is a widely used microcontroller board that serves as the

25
 Figure 4.1.6: Arduino UNO

foundation for many electronic projects. It features the ATmega328P micro-

controller, which operates at a clock speed of 16 MHz. The board offers a
range of digital and analog input/output pins that allow users to connect
and interface with various electronic components. With 14 digital I/O pins,
including 6 that support PWM, users can control LEDs, motors, and other
devices. The 6 analog input pins enable the Uno to read analog voltage val-
ues from sensors, enabling applications that require precise measurements.
The microcontroller provides 32KB of Flash memory for program storage,
2KB of SRAM for data storage, and 1KB of EEPROM for non-volatile data
storage. The Uno can be powered either via a USB connection to a com-
puter or USB power source, making it convenient for both prototyping and
standalone projects. Its versatility, ease of use, and extensive community
support have made the Arduino Uno a popular choice for hobbyists, stu-
dents, and professionals alike.
The Arduino Uno has a total of 20 pins, each serving a specific purpose.
Here is a breakdown of the pin configuration:

1. Digital I/O Pins (14): The Arduino Uno has 14 digital I/O pins, labeled
as digital pins 0 to 13. These pins can be used for both digital input
and output operations. They support 5V logic levels and can provide
or receive a maximum of 40mA of current.

2. PWM (Pulse Width Modulation) Pins (6): Among the 14 digital I/O
pins, 6 of them (pins 3, 5, 6, 9, 10, and 11) can be configured as PWM
outputs. These pins are marked with a tilde ( ) symbol on the board.
PWM allows you to simulate analog output by varying the duty cycle
of the digital signal.

3. Analog Input Pins (6): The Arduino Uno has 6 analog input pins, la-
beled as A0 to A5. These pins can read analog voltage values ranging
from 0V to 5V. They can be used with sensors or other devices that
provide analog output signals.

4. Power Pins:

26
 (a) 5V Pin: This pin provides a regulated 5V supply for powering
external components.
(b) 3.3V Pin: This pin provides a regulated 3.3V supply.
(c) GND Pins: There are several ground (GND) pins available on the
board for completing electrical circuits.

5. Special Function Pins:

(a) Reset Pin: This pin is used to reset the microcontroller. Pulling
the reset pin LOW resets the microcontroller.
(b) RX/TX Pins: These pins are used for serial communication. RX
(Receive) is pin 0, and TX (Transmit) is pin 1.

6. I2C Pins:

(a) SDA (Data): This pin is used for I2C data communication.
(b) SCL (Clock): This pin is used for I2C clock synchronization.

It’s important to note that some pins have additional functionality and can
be used for specific purposes such as SPI communication or interrupts. The
Arduino Uno’s pin configuration offers flexibility and versatility for a wide
range of projects and applications.

LCD Display
The term LCD stands for liquid crystal display. It is one kind of electronic
display module used in an extensive range of applications like various cir-
cuits and devices like mobile phones, calculators, computers, TV sets, etc.
These displays are mainly preferred for multi-segment light-emitting diodes
and seven segments. The main benefits of using this module are inexpen-
sive,simply programmable, animations, and there are no limitations for dis-
playing custom characters, special and even animations, etc.

LCD pin diagram

The 16×2 LCD pinout is shown below.

1. Pin1 (Ground/Source Pin): This is a GND pin of display, used to con-

nect the GND terminal of the microcontroller unit or power source.

2. Pin2 (VCC/Source Pin): This is the voltage supply pin of the display,
used to connect the supply pin of the power source.

3. Pin3 (V0/VEE/Control Pin): This pin regulates the difference of the

display, used to connect a changeable POT that can supply 0 to 5V.

4. Pin4 (Register Select/Control Pin): This pin toggles among command

or data register, used to connect a microcontroller unit pin and obtains
either 0 or 1(0 = data mode, and 1 = command mode).

5. Pin5 (Read/Write/Control Pin): This pin toggles the display among the
read or writes operation, and it is connected to a microcontroller unit
pin to get either 0 or 1 (0 = Write Operation, and 1 = Read Operation).

27
 Figure 4.1.7: LCD display

6. Pin 6 (Enable/Control Pin): This pin should be held high to execute

Read/Write process, and it is connected to the microcontroller unit
and constantly held high.
7. Pins 7-14 (Data Pins): These pins are used to send data to the display.
These pins are connected in two-wire modes like 4-wire mode and
8-wire mode. In 4-wire mode, only four pins are connected to the
microcontroller unit like 0 to 3, whereas in 8-wire mode, 8-pins are
connected to microcontroller unit like 0 to 7.
8. Pin15 (+ve pin of the LED): This pin is connected to +5V
9. Pin 16 (−ve pin of the LED): This pin is connected to GND.

Features of LCD16∗2
The features of this LCD mainly include the following.
1. The operating voltage of this LCD is 4.7V-5.3V.
2. It includes two rows where each row can produce 16-characters.
3. The utilization of current is 1mA with no backlight.
4. Every character can be built with a 5×8 pixel box.
5. The alphanumeric LCDs alphabets and numbers.
6. Is display can work on two modes like 4-bit and 8-bit
7. These are obtainable in Blue and Green Backlight.
8. It displays a few custom generated characters.

Registers of LCD
A 16×2 LCD has two registers like data register and command register. The
RS (register select) is mainly used to change from one register to another.
When the register set is ‘0’, then it is known as command register. Similarly,
when the register set is ‘1’, then it is known as data register.

28
 • Command Register
The main function of the command register is to store the instructions
of command which are given to the display. So that predefined tasks
can be performed such as clearing the display, initializing, set the
cursor place, and display control. Here commands processing can
occur within the register.

• Data Register
The main function of the data register is to store the information which
is to be exhibited on the LCD screen. Here, the ASCII value of the
character is the information which is to be exhibited on the screen
of LCD. Whenever we send the information to LCD, it transmits to
the data register, and then the process will be starting there. When
register set =1, then the data register will be selected.

Joystick
The joystick is an input device. The analogue joystick is sometimes called
a control stick. Used to control the movement of the pointer on the 2-
dimensional axis. The joystick has two potentiometers for reading user in-
put. A potentiometer is used to get the analogue output voltage for the X
Direction movement. The other potentiometer is used to get the analogue
output voltage for moving in the Y direction. The potentiometers are con-
nected between + VCC and the ground. They only behave like a voltage
divider network. The joystick has a rotatable holder. According to the move-
ment of the bracket, the potentiometer button changes its position and the
resistance of the potentiometer.

Figure 4.1.8: Joystick

Pin Description
1. Pin 1, 5 – VCC and GND Supply voltage(+5V) and the ground were
given to Joystick.

29
 2. Pin 2 –X-OUT This pin provides an analogue output voltage from 0
volts to VCC according to the movement of Holder in X-direction (axis).

3. Pin 3 – Y-OUT This pin provides an analogue output voltage from 0

volts to VCC according to the movement of Holder in Y- Y-direction
(axis).

4. Pin 4 – Switch This pin has one tactile switch. When a switch is not
pressed, this pin is connected to VCC through a resistor. When a
switch is pressed, this pin is connected to Ground.

4.2 Design
Sampling is the conversion of a continuous-time signal into a discrete-time
signal in signal processing. The transformation of a sound wave into a series
of ”samples” is a typical example. The definition of a sample differs from the
use of the term in statistics, which refers to a collection of such values. A
sample is a value of the signal at a point in time and/or space.
The sampling frequency or sampling rate, abbreviated fs , is the average
number of samples obtained in one second; as a result, fs = T1 , with the
unit samples per second, also known as hertz; for instance, fs = 48,000
samples per second for a sampling frequency of 48 kHz.

Design Considerations
Variable Value Description
fs 1MHz Sampling frequency
fc 10MHz Starting frequency
B 50MHz Bandwidth
Ton 10µs Chirp duration
T 1000 Total number of samples per chirp
t 1ns Sampling interval
MI 1tera Hzs−1 Modulation Index
n 2 Number of chirps
R 100m Target distance in meters
v 13ms−1 Speed of the target object

Transmitted and received signal is generated in MATLAB considering above

parameters.The target’s initial position and velocity are defined and it is
considered that velocity remains constant.
The design parameters and specifications of the Frequency Modulated
Continuous Wave (FMCW) radar analysis are presented. These parameters
include the sampling frequency, starting frequency, bandwidth, chirp du-
ration, total number of samples per chirp, sampling interval, modulation
index, number of chirps, target distance, and speed of the target object.
Sampling Frequency (fs ): The radar operates at a sampling frequency of 1
MHz, which determines the rate at which the radar samples the received
signals.

30
 • Starting Frequency (fc ): The radar starts at a frequency of 10 MHz,
serving as the initial frequency of the chirp waveform.

• Bandwidth (B): The radar employs a bandwidth of 50 MHz, indicating

the range of frequencies covered by the chirp waveform.

• Chirp Duration (Ton ): The chirp duration is set to 10 µs, representing

the time it takes for the frequency of the chirp waveform to sweep from
the starting frequency to the final frequency.

• Total Number of Samples per Chirp (T): The radar utilizes a total of
1000 samples per chirp, capturing the time-domain representation of
the received signal.

• Sampling Interval (t): The sampling interval is set to 1 ns, determining

the time between consecutive samples of the received signal.

• Modulation Index (MI): The modulation index is specified as 1 tera

Hzs−1 , indicating the rate at which the frequency of the chirp wave-
form changes over time.

• Number of Chirps (n): The radar performs 2 chirps, allowing for mul-
tiple measurements and improved detection accuracy.

• Target Distance (R): The target distance is set to 100 meters, repre-
senting the distance between the radar and the target object.

• Speed of the Target Object (v): The target object is moving at a speed
of 13 m/s, indicating the rate of change of its position over time.

These design parameters provide the necessary specifications for the FMCW
radar analysis, allowing for the accurate detection, range estimation, and
velocity measurement of the target objects. The specific values chosen for
each parameter are based on the requirements of the analysis and can be
adjusted to suit different application scenarios.
The design considerations for the hardware blocks required in the analysis
of the Frequency Modulated Continuous Wave (FMCW) radar system are
presented. The following hardware blocks are essential for the successful
implementation of the radar:

• Transmitter Block:

– Frequency Generator: Generates the starting frequency (fc) and

controls the frequency modulation.
– Modulator: Modulates the generated frequency to produce the
chirp waveform with the desired characteristics, such as chirp
duration (Ton) and modulation index (MI).
– Power Amplifier: Amplifies the modulated chirp signal to the de-
sired transmit power level.

• Antenna Block:

– Transmit Antenna: Transmits the modulated chirp signal into

the environment.

31
 – Receive Antenna: Collects the reflected signals from the target
objects.

• Receiver Block:

– Low Noise Amplifier (LNA): Amplifies the weak received signals

while maintaining a low noise figure.
– Mixer: Mixes the instantaneous received signals with instanta-
neous transmitted signal to down-convert the frequency.
– Intermediate Frequency (IF) Amplifier: Amplifies the down-converted
signals to a suitable level for further processing.
– Analog-to-Digital Converter (ADC): Converts the analog IF signals
into digital form for digital signal processing.
– Digital Signal Processor (DSP): Performs various signal process-
ing operations, including range estimation, Doppler processing,
and target detection.
– Microcontroller/Processor: Controls and coordinates the opera-
tion of the radar system, including the hardware blocks and data
processing algorithms.

• Power Supply:

– DC Power Supply: Provides the required power to operate the

hardware blocks of the radar system.
– Voltage Regulators: Stabilizes the voltage levels and ensures proper
power distribution to different components.

• Interface and Communication:

– Serial Communication Interface: Facilitates communication be-

tween the radar system and external devices, such as a computer
or user interface.
– Display and User Interface: Provides visual feedback and user
interaction for controlling and monitoring the radar system.

Together, these hardware components give the FMCW radar system the abil-
ity to transmit and receive signals, process the data that is received, and
carry out target detection, range estimation, and velocity measurement.
Specific component selection and integration will depend on elements like
desired performance, system constraints, and resource availability.
To achieve dependable and accurate operation of the FMCW radar sys-
tem, it is crucial to ensure proper integration and compatibility of the hard-
ware blocks. To improve the overall performance of the radar system, factors
like power consumption, signal integrity, and noise management should be
taken into account during the design and implementation phases.
It is important to note that the hardware blocks discussed in this de-
sign chapter, which are crucial for the functioning of the Frequency Modu-
lated Continuous Wave (FMCW) radar system, are not physically available.
Instead, all the blocks and their functionalities are simulated using MAT-
LAB. This simulation-based approach allows for a comprehensive analysis

32
of the radar system’s performance and behavior without the need for physi-
cal hardware components.By utilizing MATLAB simulations, the design and
evaluation of the FMCW radar system can still be effectively conducted. The
simulated hardware blocks, including the transmitter, receiver, antennas,
and signal processing components, enable the examination of various radar
parameters, such as range estimation, target detection, and velocity mea-
surement.
While the physical implementation of the hardware blocks would typi-
cally be necessary for practical deployment, the simulation-based approach
offers a valuable and cost-effective means for initial design, performance
evaluation, and algorithm development. It allows for extensive testing and
optimization before progressing to the physical hardware implementation
phase.
Moreover, MATLAB simulations provide flexibility in adjusting different
parameters and configurations of the FMCW radar system, facilitating thor-
ough analysis and exploration of various scenarios and operational condi-
tions.
It is crucial to acknowledge the reliance on simulated hardware blocks
and to recognize that the subsequent steps would involve transitioning from
simulation to physical implementation, considering factors such as hard-
ware selection, integration, and real-world constraints. However, the sim-
ulated results obtained from MATLAB provide valuable insights and serve
as a foundation for the subsequent stages of designing and developing the
FMCW radar system.

33
Chapter 5

Results and discussion

This chapter contains all the results obtained from the project

5.1 Generation of Transmitted and received

signal
The transmitted signal as carrier frequency of 1MHz and increases till 60MHz
(i.e. Bandwidth of 50MHz)with sweep duration of 10µs.The received signal
is time delayed replica of transmitted signal.The resulting signals are shown
in Figure 5.1.1

Figure 5.1.1: FMCW transmitted and received signal

Figure 5.1.2: Enlarged view of transmitted and received signal

34
5.2 Generation of beat signal
The beat signal is result of time domain mixing of instantaneous transmit-
ted signal and instantaneous received signal.It consists of a high frequency
signal (f1 + f2 ) and low frequency signal (f1 − f2 ).

Figure 5.2.1: Beat signal in time domain

Figure 5.2.2: Enlarged view of Beat signal in time domain

35
 Figure 5.2.3: Beat signal in frequency domain

Figure 5.2.4: Beat signal in frequency domain only lower frequencies

Then the beat signal is transformed into frequency domain and half of
the samples are filtered by removing them, resulting retention of only lower
frequency in the beat signal, frequency domain of the resultant beat signal
is as shown in Figure 5.2.4

5.3 1-D FFT output

The 1 dimentional FFT is applied on the resultant beat signals which is
stored in the matrix having beat signal frequency values through which
range can be determined based on the equation 3.1.2

36
Figure 5.3.1: FFT output versus range plot target distance considered
100m

Figure 5.3.2: FFT output versus range plot target distance considered
150m

FFT output is plotted against range.In the plot range corresponding to

the peak of FFT output represents the target distance.Different distances
were measured simulating and shown in figure 5.3.1 and 5.3.2

37
5.4 Velocity,Range and 2-D FFT

Figure 5.4.1: FFT output vs range plot

This plot provides information about the target distance. It is found in the
range axis corresponding to the highest amplitude of the 1-D FFT output.
Here the target distance was considered as 100 meters as shown in figure
5.4.1. This plot provides information about the target velocity. It is found

Figure 5.4.2: Velocity vs 2D FFT output plot

in the doppler axis corresponding to the peak amplitude of the 2-D FFT
output. Here the velocity assumed was 13.89 m/s as shown in figure 5.4.2.

38
 Figure 5.4.3: Range vs 2D FFT output plot

This plot provides information about the target distance. It is found in

the range axis corresponding to the peak amplitude of the 2-D FFT output.
Here the distance assumed was 100 meters as shown in figure 5.4.3.

Figure 5.4.4: 3D plot of 2D velocity and range v/s 2D FFT output

Figure 5.4.4 is 3 dimensional plot obtained by applying 2 dimensional

FFT on the range matrix which is obtained previously by 1-D FFT on the
beat signal. Here the velocity obtained is nearly same as assumed in the
beginning.

39
 Figure 5.4.5: Transmitter and Receiver

The nRF24L01+ is the part number of a common chip used to construct

2.4 GHz transmitters and receivers, or “transceivers”. This chip has been
used to create some simple and inexpensive modules that can be used to
transmit and receive data using the 2.4 GHz band.There are a variety of
modules available based upon the nRF24L01, and two commonly used ones
will be discussed in this article. If the reader has a different module, it
should work fine; however, it is important to observe the wiring and, par-
ticularly, the power supply requirements.

Figure 5.4.6: Transmitter

The two modules being used in this context are quite similar and can
be interchanged. The main distinction between them is that one module
includes a built-in Low Noise Amplifier (LNA) and an external antenna con-
nection. Despite being slightly more expensive, this module is preferred
due to its ability to facilitate reliable data communications over a signifi-
cant distance. For most users, it should provide ample coverage for their
entire house unless they reside in an exceptionally large property like a cas-
tle.
The nRF24L01 module is equipped with an 8-pin connector that serves
as its interface to the external environment. This connector is shared by
both styles of nRF24L01 modules. While the nRF24L01 requires a power
supply ranging from 1.9 to 3.9 volts, its logic pins have a 5-volt tolerance.
Therefore, they can be directly connected to an Arduino or any other 5-volt
logic microcontroller.

40
Chapter 6

Conclusion

6.1 Conclusion
As you can see, an FMCW sensor uses a combination of RF, analogue, and
digital electronic components to estimate the distance, speed of nearby ob-
jects. A unique class of radar technology known as millimetre wave (mmWave)
employs electromagnetic waves with short wavelengths. Radar systems emit
electromagnetic wave signals, which are reflected by objects in their path.
The range, velocity, and angle of the objects can be ascertained by a radar
system by capturing the reflected signal. mmWave radars send out signals
with millimeter-wavelength wavelengths.
One benefit of this technology is that this electromagnetic spectrum
wavelength is thought to be short. In fact, the size of the system com-
ponents needed to process mmWave signals, like the antennas, is minimal.
The high accuracy of short wavelengths is another benefit. A mmWave sys-
tem operating between 76 and 81 GHz (corresponding to a wavelength of
about 4 mm) will be able to pick up movements as minute as a millimetre
or so.
A complete mmWave radar system includes transmit (TX) and receive
(RX) radio frequency (RF) components; analog components such as clock-
ing; and digital components such as analog-to-digital converters (ADCs),
microcontrollers (MCUs) and digital signal processors (DSPs). Tradition-
ally, these systems were implemented with discrete components, which in-
creased power consumption and overall system cost. System design is chal-
lenging due the complexity and high frequencies.

6.2 Future scope

To increase performance, more investigation and work should be done on
the algorithm’s FPGA implementation. The primary focus should be on cut-
ting down on the amount of time required for transpose operations, as was
mentioned in the previous section. It should be noted that the current anal-
ysis and implementation made the assumption that the subsequent radar
scans would occur without a pause in between them, allowing the vehicle’s
driver to always be aware of its surroundings. However, the provided model
does not make it very clear whether this condition is required. Therefore, if
there is sufficient time between consecutive radar processing, it should be

41
examined whether the SDRAM storage of the intermediate data is actually
necessary.

42
References

[1] A design of the frequency modulated continuous wave (FMCW) radar

system,Yoon Sung Cho and Sung Ho Cho ,Department of Electronic
and Computer Engineering ,Hanyang University.

[2] A Scanning FMCW-Radar System for the Detection of Fast Moving

Objects Sensors for Sniper Detection Purposes,A. Shoykhetbrod, A.
Hommes and N. Pohl Fraunhofer FHR ,Wachtberg, Germany.

[3] A Study of an X-band FMCW Radar for Small Object Detection.2021 In-
ternational Electrical Engineering Congress (iEECON2021) March 10-
12, 2021, Pattaya, THAILAND.

[4] Assembly of an S-band FMCW Doppler Radar System for improved

Pedestrians Detection,International Journal of Multimedia and Ubiq-
uitous Engineering Vol.12, No.3 (2017), pp.65-74.

[5] Design of an FMCW radar baseband signal processing system

for automotive application,Lin et al. SpringerPlus (2016) 5:42 DOI
10.1186/s40064-015-1583-5.

[6] Design of FMCW Radars for Active Safety Applications.

[7] Obstacle Avoidance onboard MAVs using a FMCW RADAR Nikhil

Wessendorp, Raoul Dinaux, Julien Dupeyroux and Guido C. H. E. de
Croon

[8] FMCW Radar Phase-Processing for Automotive Application,Karlisa

Priandana, Alex Coccia, Leo P. Ligthart

[9] Multi-Target Detection Algorithm for FMCW Radar.Eugin Hyun,Woojin

Oh,Jong-Hun Lee

[10] Low-Cost S-Band FMCW Radar-Based Short-Range Displacement Sen-

sor.Filip Rosu,Andrei Anghel.

[11] Low-cost C-band FMCW Radar System comprising Commercial Off-

the-shelf Components for Indoor Through-wall Object Detection.
Hyunmin Jeong and Sangkil Kim

[12] Software Design to Simulate FMCW Radar Signal: A Case Study of

INDERA,Wahju Sediono , Andrian A. Lestari

[13] Software Design to Simulate FMCW Radar Signal: A Case Study of

INDERA,Wahju Sediono , Andrian A. Lestari

43
[14] A Software-Synchronization Based, Flexible, Low-Cost FMCW
Radar.TAO WANG , PING LI , RUI WANG , ZHICHAO SHENG AND
LIXUN HUANG

[15] Design of new frequency modulated continuous wave(FMCW) target

tracking radar with digital beam forming tracking.

[16] Vehicle detection and warning system using RADAR technology.Neeraj

kumar Agarwal,Sumit Thakur,Arun Kumar Singh.

[17] Study of an X-band FMCW Radar for Small Object Detection.

[18] A Scanning FMCW-Radar System for the Detection of Fast Moving Ob-
jects.

[19] Assembly of an S-band FMCW Doppler Radar System for improved

Pedestrians Detection.

[20] X-Band Front-end Module of FMCW RADAR for Collision Avoidance

Application.Tiwat Pongthavornkamol, Aman Worasutr, Denchai Wora-
sawate, La-or Kovavisaruch and Kamol Kaemarungsi

44