vi
CONTENTS
4.3
The architecture and operation
. . . . . . . . . . . . . . . . .
33
5
Results and Analysis
39
5.1
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
5.1.1
Hardware Resource Usage . . . . . . . . . . . . . . . .
39
5.1.2
Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
5.1.3
Performance . . . . . . . . . . . . . . . . . . . . . . . .
41
5.2
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
43
5.2.1
Evaluation . . . . . . . . . . . . . . . . . . . . . . . . .
43
6
Conclusion
47
6.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
6.2
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
List of Figures
1.1
FMCW radar block diagram . . . . . . . . . . . . . . . . . . .
4
1.2
Xilinx ML605 development board . . . . . . . . . . . . . . . .
5
1.3
NXP Semiconductor’s automotive radar chip
. . . . . . . . .
7
2.1
FMCW sawtooth signal model . . . . . . . . . . . . . . . . . .
9
2.2
FMCW signal 2D FFT processing . . . . . . . . . . . . . . . .
14
2.3
Principle of phase interferometry [1] . . . . . . . . . . . . . . .
14
2.4
TX and RX antennas of MIMO radar . . . . . . . . . . . . . .
16
2.5
Virtual antenna array . . . . . . . . . . . . . . . . . . . . . . .
17
3.1
Range-Doppler Spectrum . . . . . . . . . . . . . . . . . . . . .
20
3.2
Birdseye view . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
3.3
Radar scannings . . . . . . . . . . . . . . . . . . . . . . . . . .
24
3.4
Signal Flow Graph of 3D FFT Procesing . . . . . . . . . . . .
25
4.1
Signal processing algorithm flowchart . . . . . . . . . . . . . .
31
4.2
The architecture of the implementation . . . . . . . . . . . . .
34
4.3
An example transpose operation . . . . . . . . . . . . . . . . .
35
5.1
Processes and their performance . . . . . . . . . . . . . . . . .
43
vii
List of Tables
2.1
Parameter table . . . . . . . . . . . . . . . . . . . . . . . . . .
12
5.1
Resource usage of the architecture . . . . . . . . . . . . . . . .
40
5.2
Radar test results . . . . . . . . . . . . . . . . . . . . . . . . .
41
5.3
Timing results of the implementation . . . . . . . . . . . . . .
42
ix
List of Acronyms
ADC Analog to Digital Converter
AXI Advanced eXtensible Interface
CAES Computer Architecture for Embedded Systems
CLB Configurable Logic Block
CPU Central Processing Unit
CW Continuous Wave
DDR Double Data Rate
DFT Discrete Fourier Transform
DMA Direct Memory Access
DSP Digital Signal Processing
DVI Digital Visual Interface
FFT Fast Fourier Transform
FIFO First-In First-Out
FMCW Frequency Modulated Continuous Wave
FPU Floating Point Unit
FPGA Field-Programmable Gate Array
LMB Local Memory Bus
MIMO Multiple Input Multiple Output
MPSoC Multiprocessor System-on-Chip
xi
xii
LIST OF ACRONYMS
NoC Network on Chip
RF Radio Frequency
SDRAM Synchronous Dynamic Random-Access Memory
SODIMM Small Outline Dual In-line Memory Module
TDM Time-Division Multiplexing
UART Universal Asynchronous Receiver/Transmitter
WCET Worst Case Execution Time
Chapter 1
Introduction
1.1
Context
For a long time radars have been used in multiple military and commercial
applications. The development of the ideas that lead to the radar systems
emerged in the late nineteenth and early twentieth centuries. However, the
main developments of the system have been seen during the Second World
War. During that period radars were extensively used for air defence pur-
poses such as long-range air surveillance and short-range detection of low
altitude targets. In the post-war period, improvements had been made in
the development of the radar technology for both the military and civilian
applications. Major civilian applications of the radar that emerged during
that period were the weather radar and the air-traffic control radar that used
to ensure the safety of the air traffic in the airports [2].
Recently, applications of radars in the automotive industry have started
to emerge. High-end automobiles already have radars that provide parking
assistance and lane departure warning to the driver [3]. Currently, there is
a growing interest in the self-driving cars and some people consider it to be
the main driving force of the automotive industry in the coming years. With
the start of the Google’s self-driving car project, the progress in this area has
got a new acceleration.
Self-driving cars offer a totally new perspective on the application of the
radar technology in the automobiles. Instead of only assisting the driver,
the new automotive radars should be capable of taking an active role in the
control of the vehicle. As a matter of fact, they will be a key sensor of the
autonomous control system of a car.
Radar is preferred over the other alternatives such as sonar or lidar as
it is less affected by the weather conditions and can be made very small to
1