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Advanced Signal Processing for Automotive Radar Systems : 차량용 레이더 시스템을 위한 신호 처리 기법

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dc.contributor.advisor김성철-
dc.contributor.author이성욱-
dc.date.accessioned2018-11-12T00:53:22Z-
dc.date.available2018-11-12T00:53:22Z-
dc.date.issued2018-08-
dc.identifier.other000000153056-
dc.identifier.urihttps://hdl.handle.net/10371/142980-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2018. 8. 김성철.-
dc.description.abstractRecently, as automobile safety has been receiving considerable public attention, sensors devised for automobiles, such as sonar, vision, lidar, and radar systems, have become significant. Among these sensors, the radar is robust to harsh environmental conditions, such as no-light conditions or bad weather. The automotive radar systems, mounted on automobiles, perform special functions such as adaptive cruise control, autonomous emergency braking, and blind spot detection for driver safety and convenience.



In this dissertation, advanced signal processing techniques for automotive radar systems are proposed. In general, frequency-modulated continuous wave (FMCW) radar systems are widely used for automotive radars. The main purpose of using the automotive FMCW radar is to extract the information of targets, such as relative distances, relative velocities, and angles. In automotive radar systems, estimating the angle of the target is a challenging problem because the number of receiving antenna elements is limited. Therefore, an enhanced target angle estimation method using signal-to-noise (SNR) compensation or array interpolation is proposed in this dissertation. In addition to basic target detection, automotive radar systems aim to perform more advanced functions. For example, the automotive radar should be able to classify the detected targets. Thus, a method to classify the targets, such as pedestrians, cyclists, and vehicles, is proposed in the dissertation. In addition, target detection performance of the automotive radar can be degraded in road structures, such as iron tunnels and soundproof walls. Therefore, this dissertation proposes a method to recognize such road structures and to suppress their adverse effects. Moreover, as the number of radar-equipped vehicles increases in the near future, mutual interference among automotive radars can cause a serious problem because it degrades the target detection performance. Therefore, a method for mitigating the mutual interference is also proposed in this dissertation.
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dc.description.tableofcontents1 FUNDAMENTALS OF AUTOMOTIVE FMCW RADAR SYSTEMS 1

2 TWO-STAGE DIRECTION OF ARRIVAL ESTIMATION METHOD FOR LOW SNR SIGNALS 4

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 DOA Estimation in Array Antenna . . . . . . . . . . . . . . . . . . . 6

2.2.1 Signal Model for Array Antenna . . . . . . . . . . . . . . . . 6

2.2.2 Subspace-Based DOA Estimation Algorithms . . . . . . . . . 7

2.3 Proposed Two-Stage DOA Estimation . . . . . . . . . . . . . . . . . 8

2.3.1 Stage 1: Coarse DOA Estimation . . . . . . . . . . . . . . . . 9

2.3.2 Stage 2: Fine DOA Estimation . . . . . . . . . . . . . . . . . 10

2.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Measurement Results . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3 LOGARITHMIC-DOMAIN ARRAY INTERPOLATION FOR IMPROVED DIRECTION OF ARRIVAL ESTIMATION 23

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2 Conventional Array Interpolation Method . . . . . . . . . . . . . . . 25

3.3 Logarithmic-Domain Array Interpolation . . . . . . . . . . . . . . . 27

3.3.1 Proposed Array Interpolation Method . . . . . . . . . . . . . 27

3.3.2 Enhanced Received Signal Interpolation . . . . . . . . . . . . 29

3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.5 Measurement Results . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4 TARGET CLASSIFICATION USING FEATURE-BASED SUPPORT VECTOR MACHINE 42

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.2 Introduction of Root Radar Cross Section (RRCS) . . . . . . . . . . . 45

4.3 Data Measurement with FMCW Radar . . . . . . . . . . . . . . . . . 49

4.3.1 Measurement Campaign . . . . . . . . . . . . . . . . . . . . 49

4.3.2 Statistical Characteristics of RRCS . . . . . . . . . . . . . . 52

4.4 Feature Extraction Based on RRCS . . . . . . . . . . . . . . . . . . . 53

4.4.1 Magnitude of RRCS . . . . . . . . . . . . . . . . . . . . . . 53

4.4.2 Moving Pattern along RRCS . . . . . . . . . . . . . . . . . . 54

4.4.3 Slopes around RRCS . . . . . . . . . . . . . . . . . . . . . . 56

4.4.4 Extracted-Feature Space . . . . . . . . . . . . . . . . . . . . 56

4.5 Human-Vehicle Classification Using SVM . . . . . . . . . . . . . . . 57

4.5.1 Training and Validation of Data . . . . . . . . . . . . . . . . 57

4.5.2 Classification Results . . . . . . . . . . . . . . . . . . . . . . 58

4.5.3 Real-Time Target Classification . . . . . . . . . . . . . . . . 60

4.6 Application to More Practical Situation . . . . . . . . . . . . . . . . 61

4.6.1 Other Types of Targets . . . . . . . . . . . . . . . . . . . . . 61

4.6.2 Target Classification in Real Road Environment . . . . . . . . 61

4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5 STATISTICAL CHARACTERISTIC-BASED ROAD STRUCTURE RECOGNITION AND CLASSIFICATION 64

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.2 Beat Frequencies in Periodic Road Structures . . . . . . . . . . . . . 67

5.3 Measurement of Radar Signals in Actual Road Environments . . . . . 69

5.3.1 Specifications of Automotive FMCW Radar Used in Measurements . . . . 69

5.3.2 Received Radar Signal Analysis Method for Measured Data . 71

5.4 Proposed Road Structure Recognition Method . . . . . . . . . . . . . 75

5.4.1 Distribution Fitting of Frequency Components . . . . . . . . 75

5.4.2 Parameters Representing Statistical Characteristics . . . . . . 77

5.4.3 Road Structure Recognition Using SVM Method . . . . . . . 81

5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

6 PERIODIC CLUTTER SUPPRESSION IN IRON ROAD STRUCTURES 88

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6.2 Received Signal Analysis in Iron Road Structures . . . . . . . . . . . 90

6.3 Periodic Clutter Suppression in Iron Road Structures . . . . . . . . . 92

6.3.1 Proposed Periodic Clutter Suppression Method . . . . . . . . 92

6.3.2 Clutter Suppression Results . . . . . . . . . . . . . . . . . . 95

6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

7 MUTUAL INTERFERENCE SUPPRESSION USING WAVELET DENOISING 103

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

7.2 Effect of Mutual Interference on Beat Frequency Estimation . . . . . 106

7.3 Proposed Mutual Interference Suppression Method Using Wavelet Denoising . . . . 111

7.3.1 Decomposition of Low-pass Filter Output Using Wavelet Transform . . . . 111

7.3.2 Thresholding for Extracting Wavelet Coefficients of Interference Signal . . . . . . . . . . 112

7.3.3 Reconstruction of Interference Signal . . . . . . . . . . . . . 114

7.3.4 Subtracting Reconstructed Interference Signal from Original Low-pass Filter Output . 114

7.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

7.5 Measurement Results . . . . . . . . . . . . . . . . . . . . . . . . . . 120

7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Abstract (In Korean) 139
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dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subject.ddc621.3-
dc.titleAdvanced Signal Processing for Automotive Radar Systems-
dc.title.alternative차량용 레이더 시스템을 위한 신호 처리 기법-
dc.typeThesis-
dc.contributor.AlternativeAuthorSeongwook Lee-
dc.description.degreeDoctor-
dc.contributor.affiliation공과대학 전기·컴퓨터공학부-
dc.date.awarded2018-08-
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