328 research outputs found
System Design of Advanced Multi-Beam and Multi-Range Automotive Radar
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.As the number of vehicles on the road is increased, the incidence of traffic accident
is gradually increased and the number of death on roads is also increased. Most
accidents are due to carelessness of the driver. If the vehicle can actively recognize
the dangerous situation and alert the driver to avoid accident, it will be a great help
to the driver. As concern for safety and driver assistance increases, needs for driver
assistance system (DAS) are consistently increasing. Moreover, with the grooming demand
for autonomous driving, there has been paid a great attention to the incorporation
of multiple sensors. Various sensors for safety and convenience are being introduced
for automobiles. The detection performance of the automotive radar looks outstanding
compared to other sensors such as Lidar, camera, and ultrasonic sensors, in poor
weather conditions or environmental conditions of the roads. Among many applications
using automotive radars, the adaptive cruise control (ACC) and the autonomous
emergency braking (AEB) using forward looking radars are the most basic functions
for safety and convenience. Using ACC and AEB functions, drivers can be guaranteed
safety as well as convenience when visibility is poor under bad weather conditions.
Generally, the radar system for ACC and AEB had been composed of singe longrange
radar (LRR) and two of short-range radar (SRR) and the system cost was very
expensive. However, the cost can be lowered by the concept of multi-beam, multirange
(MBMR) radar which consist of integrated narrow long range beam and wide
short range beam in a single radar sensor.
In this dissertation, we propose an advanced MBMR radar for ACC and AEB using
77 GHz band and highly integrated RF ICs. The detection specifications are investii
gated base on theoretical radar principles and effective design concepts are suggested
to satisfy the specifications. We implemented an actually working forward looking
MBMR radar and performed experiments to verify the detection performance.
To overcome the limitation of radar hardware resources for cost-effective design,
we propose novel signal processing schemes to recognize environment on roads which
are regarded as impossible with automotive radar. Characteristics of an iron tunnel
which deteriorate the detection performance of the radar are analyzed and a measure
for the recognition is proposed.
Moreover, the recognition method is expanded to harmonic clutters which are
caused by man-made structures on roads containing periodic structures such as iron
tunnels, guardrails, and sound-proof wall. The harmonic clutter suppression method is
also proposed to enhance the quality of the received signal and improve the detection
performance of the radar.
All experiments are performed using the proposed MBMR radar to verify the detection
performance and the usefulness of proposed signal processing methods for
recognition and suppression of clutters on roads.1 Introduction 1
2 A Multi-Beam and Multi-Range FMCW Radar using 77 GHz Frequency Band for ACC and AEB 6
2.1 Introduction 6
2.2 System Design of Advanced MBMR Radar 7
2.3 Waveform and Signal Processing Structure Design 14
2.4 Advanced Singal Processing Technique for AEB 19
2.5 Design Results 20
2.6 Experimental Results 22
2.6.1 Anechoic Chamber 22
2.6.2 Field Test 27
2.7 Summary 29
3 Iron-tunnel Recognition 30
3.1 Introduction 30
3.2 Iron-Tunnel Recognition 32
3.2.1 Radar Model 32
3.2.2 Spectral Characteristics of an Iron-Tunnel 34
3.2.3 Measuring Spectrum Spreading 40
3.3 Experimental Result 45
3.3.1 Iron-Tunnel Recognition 45
3.3.2 Early Target Detection and Prevention of Target Drop 49
3.4 Summary 53
4 Clutter Suppression 55
4.1 Introduction 55
4.2 Clutter Recognition 57
4.2.1 Radar Model 57
4.2.2 Spectral Analysis of Road Environment 62
4.2.3 Proposed Clutter-recognition Method (Measuring Harmonics of Clutter) 64
4.3 Clutter Suppression 69
4.3.1 Proposed clutter suppression method 69
4.3.2 Verification using real data 71
4.4 Experimental results 74
4.5 Summary 81
5 Conclusion and Future Works 82
Bilbliography 85
Abstract (In Korean) 89Docto
EFFICIENT PARAMETER ESTIMATION METHODS FOR AUTOMOTIVE RADAR SYSTEMS
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 김성철.As the demand for safety and convenience in the automotive-technology field increased, many applications of advanced driving assistance systems were developed. To provide driving information, among the sensors, such as cameras sensor, light detection and ranging sensor, radar sensor, and ultrasonic sensor, a radar sensor is known to exhibit excellent performance in terms of visibility for different weather conditions. Especially with the legislation of the adaptive cruise control system and autonomous emergency braking system in a global environment, the market of the automotive radar sensor is expected to grow explosively. At present, the development of cost-effective radar offering high performance with small size is required. In addition, the radar system should be enforced to have a simultaneous functionality for both long and short ranges. Thus, challenging issues still remain with respect to radar signal processing including high-resolution parameter estimation, multi-target detection, clutter suppression, and interference mitigation.
For high-resolution parameter estimation, direction-of-arrival (DOA) estimation method has been investigated to identify the target object under complex unban environment. To separate closely spaced target having similar range and distance, high-resolution techniques, such as multiple signal classification (MUSIC), the estimation of signal parameters via rotational invariance techniques (ESPRIT), and maximum likelihood (ML) algorithm, are applied for automotive radars. In general, cycle time for radar system, which is the processing time for one snapshot, is very short, thus to establish a high-resolution estimation algorithm with computational efficiency is additional issue.
On the other hands, multi-target detection scheme is required to identify many targets in the field of view. Multi-target detection is regarded as target pairing solution, whose task is to associate frequency components obtained from multiple targets. Under certain conditions, the association may fail and real target may be combined to ghost components. Thus, reliable paring or association method is essential for automotive radar systems.
The clutter denotes undesired echoes due to reflected wave from background environment, which includes guardrail, traffic signs, and stationary structures around the load. To minimize the effect of clutter, conventional radar systems use high pass filter based on the assumption that the clutter is stationary with energy concentrated in the low frequency domain. However, the clutter is presented with various energy and frequency under automotive radar environment. Especially, under the specific environment with iron materials, target component is not detected due to clutter with large power.
Mutual interference is a crucial issue that must be resolved for improved safety functions. Given the increasing number of automotive radar sensors operating at the same instant, the probability that radar sensors may receive signals from other radar sensors gradually increases. In such a situation, the system may fail to detect the correct target given the serious interference. Effective countermeasures, therefore, have to be considered.
In this dissertation, we propose efficient parameter estimation methods for automotive radar system. The proposed methods include the radar signal processing issues as above described, respectively. First, the high-resolution DOA estimation method is proposed by using frequency domain analysis. The scheme is based on the MUSIC algorithm, which use distinct beat frequency of the target. The target beat frequency also gives distance and velocity. Thus, the proposed algorithm provides either high-resolution angle information of target or natural target pairing solution. Secondly, we propose the clutter suppression method under iron-tunnel conditions. The clutter in iron-tunnel environments is known to severely degrade the target detection performance because of the signal reflection from iron structures. The suppression scheme is based on cepstral analysis of received signal. By using periodical characteristic of the iron-tunnel clutter, the suppressed frequency response is obtained. Finally, the interference mitigation scheme is studied. Mutual interference between frequency modulated continuous waveform (FMCW) radars appears in the form of increased noise levels in the frequency domain and results in a failure to separate the target object from interferer. Thus, we propose a high-resolution frequency estimation technique for use in interference environments.Chapter 1. Introduction 1
1.1 Background 1
1.2 ADAS Applications for Automotive Radar 3
1.3 Motivation and Organization 5
Chapter 2. High-Resolution Direction-of-Arrvial Estimation with Pairing function for Automotive Radar Systems 8
2.1 Introduction 8
2.2 High-Resolution DOA Estimation for automotive Radars 10
2.2.1 DOA Estimation in the Time-domain Processing 11
2.2.2 DOA Estimation in the Frequency-domain Processing 15
2.3 Simulation Result 18
2.3.1 Simulation setup 18
2.3.2 Performance Comparison of the DOA Estimation in Time- and Frquency-domain Processing 19
2.3.3 Performance Analysis of the DOA Estimation in Frequency-domain 23
2.4 Conclusion 26
Chapter 3. Clutter Suppression Method of Iron Tunnel using Cepstral Analysis for Automotive Radars 27
3.1 Introduction 27
3.2 Clutter Suppression under Iron Tunnels 30
3.2.1 Radar Model of an Iron Tunnel 30
3.2.2 Cepstrum Analysis of an Iron Tunnel 33
3.2.3 Cepstrum Based Clutter Suppression Method 36
3.3 Experimental Result 39
3.4 Conclusion 46
Chapter 4. Interference Mitigation by High-Resolution Frequency Estimation in Automotive FMCW Radar 47
4.1 Introduction 47
4.2 Automotive FMCW Radars in an Interference Environment 50
4.2.1 The Same Sign-Chirp Case 54
4.2.2 The Different Sign-Chirp Case 56
4.3 High-Resolution Frequency Estimation Method 58
4.3.1 Data Model 58
4.3.2 Estimation of Correlation Matrix 61
4.3.3 Application of the MUSIC Algorithm 62
4.3.4 Application of the MUSIC Algorithm 63
4.3.5 Number of Frequency Estimation 65
4.4 Experimental Result 66
4.5 Conclusion 71
Bibliography 72
Abstract in Korean 78Docto
Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain
A significant challenge in autonomous driving systems lies in image
understanding within complex environments, particularly dense traffic
scenarios. An effective solution to this challenge involves removing the
background or static objects from the scene, so as to enhance the detection of
moving targets as key component of improving overall system performance. In
this paper, we present an efficient algorithm for background removal in
automotive radar applications, specifically utilizing a frequency-modulated
continuous wave (FMCW) radar. Our proposed algorithm follows a three-step
approach, encompassing radar signal preprocessing, three-dimensional (3D)
ego-motion estimation, and notch filter-based background removal in the
azimuth-elevation-Doppler domain. To begin, we model the received signal of the
FMCW multiple-input multiple-output (MIMO) radar and develop a signal
processing framework for extracting four-dimensional (4D) point clouds.
Subsequently, we introduce a robust 3D ego-motion estimation algorithm that
accurately estimates radar ego-motion speed, accounting for Doppler ambiguity,
by processing the point clouds. Additionally, our algorithm leverages the
relationship between Doppler velocity, azimuth angle, elevation angle, and
radar ego-motion speed to identify the spectrum belonging to background
clutter. Subsequently, we employ notch filters to effectively filter out the
background clutter. The performance of our algorithm is evaluated using both
simulated data and extensive experiments with real-world data. The results
demonstrate its effectiveness in efficiently removing background clutter and
enhacing perception within complex environments. By offering a fast and
computationally efficient solution, our approach effectively addresses
challenges posed by non-homogeneous environments and real-time processing
requirements
On the Application of Digital Moving Target Indication Techniques to Short-Range FMCW Radar Data
In this paper, we describe three digital moving target indication (MTI) and moving target segmentation techniques (based on target speed) and apply them to short-range frequency-modulated continuous wave (FMCW) radar data. The described approaches are applicable to many short-range radar sensors. In particular, we focus on FMCW radar, which are ubiquitous in numerous applications, including gesture recognition radar, automotive radar, and imaging radar. The three digital MTI filtering methods explored are background subtraction, finite impulse response (FIR) filtering, and infinite impulse response (IIR) filtering. Each of the methods is implemented in the time domain for simpler logic implementation. We apply the MTI methods on data sets gathered using a C-band FMCW radar in both a short-range, direct line-of-sight scenario and a complex cluttered through wall radar scenario. Based on the analyses, it is shown that each of the MTI techniques are extremely effective when deployed in the right scenario. Background subtraction is found to be well suited for slow-moving targets. FIR and IIR filtering techniques provide the simplest, one-step processes for moving target segmentation
Using Machine Learning to Detect Ghost Images in Automotive Radar
Radar sensors are an important part of driver assistance systems and
intelligent vehicles due to their robustness against all kinds of adverse
conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is
achieved by a substantially larger wavelength compared to light-based sensors
such as cameras or lidars. As a side effect, many surfaces act like mirrors at
this wavelength, resulting in unwanted ghost detections. In this article, we
present a novel approach to detect these ghost objects by applying data-driven
machine learning algorithms. For this purpose, we use a large-scale automotive
data set with annotated ghost objects. We show that we can use a
state-of-the-art automotive radar classifier in order to detect ghost objects
alongside real objects. Furthermore, we are able to reduce the amount of false
positive detections caused by ghost images in some settings
Radar Technology
In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design
A Multi-Stage Clustering Framework for Automotive Radar Data
Radar sensors provide a unique method for executing environmental perception
tasks towards autonomous driving. Especially their capability to perform well
in adverse weather conditions often makes them superior to other sensors such
as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of
the commonly used detection data level is a major challenge for subsequent
signal processing. Therefore, the data points are often merged in order to form
larger entities from which more information can be gathered. The merging
process is often implemented in form of a clustering algorithm. This article
describes a novel approach for first filtering out static background data
before applying a twostage clustering approach. The two-stage clustering
follows the same paradigm as the idea for data association itself: First,
clustering what is ought to belong together in a low dimensional parameter
space, then, extracting additional features from the newly created clusters in
order to perform a final clustering step. Parameters are optimized for
filtering and both clustering steps. All techniques are assessed both
individually and as a whole in order to demonstrate their effectiveness. Final
results indicate clear benefits of the first two methods and also the cluster
merging process under specific circumstances.Comment: 8 pages, 5 figures, accepted paper for 2019 IEEE 22nd Intelligent
Transportation Systems Conference (ITSC), Auckland, New Zealand, October 201
Roadmap on signal processing for next generation measurement systems
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
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