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

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