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Statistical Characteristic-Based Road Structure Recognition in Automotive FMCW Radar Systems
Lee, Seongwook,Lee, Byeong-Ho,Lee, Jae-Eun,Kim, Seong-Cheol IEEE 2019 IEEE transactions on intelligent transportation sy Vol.20 No.7
<P>This paper proposes an efficient road structure recognition method using statistical characteristics of received signals in automotive frequency-modulated continuous wave radar systems. Generally, roads consist of various structures, some of which, such as tunnels and soundproof walls made of iron, generate undesired echoes, called clutter. When the clutter flows into the radar system, the target detection performance cannot be guaranteed completely. This causes great danger to the driver using the radar function such as adaptive cruise control. Thus, an efficient method to recognize the structures that deteriorate the radar detection performance is desired. Depending on the types of road structures, frequency components of the received signals have distinctive distributions. Focusing on this point, parameters that reflect statistical properties of each distribution are extracted. These parameters can be used as standards for the recognition because they show different values according to the road structures. For more enhanced recognition, we use a support vector machine method with a linear classifier or a Gaussian kernel, and the resulting confusion matrices are derived. According to the results, the proposed method successfully classifies the structures with high accuracy. If the recognition of the road structures that degrade radar’s function is performed effectively, the safety of the driver in the radar-equipped vehicle can be ensured by applying additional signal processing or giving a warning message to the driver.</P>
해운대 해수욕장에 설치된 해파리 차단망의 수중 안정성 분석
박성욱 ( Seongwook Park ),이동길 ( Dong Gil Lee ),양용수 ( Yong Su Yang ),이형빈 ( Hyung Been Lee ),이경훈 ( Kyoung Hoon Lee ),한민수 ( Min Soo Hahn ),이태화 ( Tae Wha Lee ) 한국수산해양기술학회 2015 수산해양기술연구 Vol.51 No.1
The worldwide abundance of various jellyfish appears to have increased in coastal ecosystems in recent years. The enormous jellyfish blooms cause a variety of problems for the local ecology, fisheries, and aquatic-sports in coastal locations. In this study, jellyfish sting protection net was installed to ensure the safety and reduction of the inflow into the Haeundae beach. In order to confirm the stability of the protection net, the tension for protection net was measured from variation of current speed. The periods for maximum tension were observed correspond to the periods of maximum current speed. The maximum tension for protection net was measured up to 4,100 kg. From field evaluations, the jellyfish sting protection net has demonstrated to stability from the current and tide in the Haeundae beach.
Human–vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar
Lee, Seongwook,Yoon, Young-Jun,Lee, Jae-Eun,Kim, Seong-Cheol IET 2017 IET radar, sonar & navigation Vol.11 No.10
<P>In this study, a human-vehicle classification using a feature-based support vector machine (SVM) in a 77-GHz automotive frequency modulated continuous wave (FMCW) radar system is proposed. As a classification criterion, the authors use a newly defined parameter called root radar cross section which reflects the reflection characteristics of targets. Based on this parameter, three distinctive signal features are extracted from frequency-domain received FMCW radar signals, and they become classification standards used for the SVM. Finally, through measurement results on the test field, the classification performance of the authors' proposed method is verified, and the average classification accuracy from a four-fold cross data validation is found to be higher than 90%. In addition, the authors' proposed classification method is applied to distinguish a pedestrian, a vehicle, and a cyclist in a more practical situation, and it also shows good classification performance.</P>
Two-stage DOA estimation method for low SNR signals in automotive radars
Seongwook Lee,Young-Jun Yoon,Jae-Eun Lee,Heonkyo Sim,Seong-Cheol Kim IET 2017 IET radar, sonar & navigation Vol.11 No.11
<P>In this study, the authors propose an effective two-stage direction-of-arrival (DOA) estimation method for low signal-to-noise (SNR) signals in automotive radar systems. When antenna elements in the array receive low SNR signals, the performance of subspace-based DOA estimation algorithms is degraded. Concerning this case, they propose an enhanced DOA estimation method that offers better angular resolution and estimation performance. The authors' proposed method is comprised of two stages. In the first stage, they roughly estimate the DOA using conventional subspace-based DOA estimation algorithms. Thereafter, the fine DOA estimation is performed in the next stage. The fine estimation includes a received signal calibration method using a priori information acquired from the previous stage. From simulation results, in terms of root mean square error and resolution probability, their proposed method exhibits a DOA estimation performance that is superior to that of the conventional method. Moreover, with actual measurement data, they verify that the proposed method can be applied to practical automotive radar systems.</P>