In-Vehicle NLOS Pedestrian Detection System using Secondary Radar based on Frequency Doubling and Oversampling Signal Analysis

被引:0
|
作者
Masuda, Satomi [1 ]
Zhou Zhongqi [1 ]
Kitamura, Maiko [1 ]
Morishita, Mao [1 ]
Jitsuno, Kunihisa [1 ]
Inagaki, Keizo [2 ]
Kanno, Atsushi [2 ]
Kawanishi, Tetsuya [1 ]
机构
[1] Waseda Univ, Sch Fund Sci & Engn, Shinjuku Ku, 3-4-1 Ookubo, Tokyo 1698555, Japan
[2] Natl Inst Informat & Comms Tech, 4-2-1 Nukui Kitamachi, Koganei, Tokyo 1848795, Japan
关键词
Secondary radar; frequency doubler; non-line-of-sight; pedestrians detection; oversampling;
D O I
10.1587/comex.2022XBL0085
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Detecting signals in areas where there are many obstacles, such as Non-Line-of-Sight (NLoS) is difficult, in conventional radar systems. In this study, we investigated a radar system for detecting pedestrians in NLoS using a secondary radar system and oversampling of the received waveform in an on-board device; then, measured the range and direction detection properties. We analyzed the results of these experiments that were measured in a large anechoic chamber with metal walls to emulate NLoS conditions. The direction of the target in the NLoS was successfully detected. The proposed scheme was able to measure the distance to the target within an error margin of 5 m.
引用
收藏
页数:6
相关论文
共 33 条
  • [1] Simple Pedestrian Detection Secondary Radar Using Frequency Doubling
    Kawanishi, Tetsuya
    Masuda, Satomi
    Shigematsu, Erika
    Jitsuno, Kunihisa
    Inagaki, Keizo
    Kanno, Atsushi
    2020 22ND INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON 2020), 2020,
  • [2] Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks
    Sriranga, Ashwini Kanakapura
    Lu, Qian
    Birrell, Stewart
    2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2022, : 55 - 60
  • [3] In-Vehicle Traffic Accident Detection and Alerting System Using Distance-Time Based Parameters and Radar Range Algorithm
    Ajao, Lukman Adewale
    Abisoye, Blessing Olatunde
    Jibril, Ibrahim Zakariyawu
    Jonah, Udeme Monday
    Kolo, Jonathan Gana
    2020 IEEE PES & IAS POWERAFRICA CONFERENCE, 2020,
  • [4] Neural Network-Based Vehicle and Pedestrian Detection for Video Analysis System
    Babayan, Pavel V.
    Ershov, Maksim D.
    Erokhin, Denis Y.
    2019 8TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2019, : 310 - 314
  • [5] Intrusion Detection System Based on the Analysis of Time Intervals of CAN Messages for In-Vehicle Network
    Song, Hyun Min
    Kim, Ha Rang
    Kim, Huy Kang
    2016 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN), 2016, : 63 - 68
  • [6] Pedestrian detection system using CENTRIST algorithm on SIMT based image signal processor
    Jeong J.-M.
    Jeon H.-K.
    Park J.-J.
    International Journal of Multimedia and Ubiquitous Engineering, 2016, 11 (12): : 257 - 264
  • [7] Experimental Analysis of Trustworthy In-Vehicle Intrusion Detection System Using eXplainable Artificial Intelligence (XAI)
    Lundberg, Hampus
    Mowla, Nishat, I
    Abedin, Sarder Fakhrul
    Thar, Kyi
    Mahmood, Aamir
    Gidlund, Mikael
    Raza, Shahid
    IEEE ACCESS, 2022, 10 : 102831 - 102841
  • [8] Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning
    Y. Méneroux
    A. Le Guilcher
    G. Saint Pierre
    M. Ghasemi Hamed
    S. Mustière
    O. Orfila
    International Journal of Data Science and Analytics, 2020, 10 : 101 - 119
  • [9] Traffic signal detection from in-vehicle GPS speed profiles using functional data analysis and machine learning
    Meneroux, Y.
    Le Guilcher, A.
    Saint Pierre, G.
    Hamed, M. Ghasemi
    Mustiere, S.
    Orfila, O.
    INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2020, 10 (01) : 101 - 119
  • [10] Real-time Aggressive Driving Detection System based on In-vehicle Information using LoRa Communication
    Jeon, Yongsu
    Kim, Chanwoo
    Lee, Hyunwook
    Baek, Yunju
    2019 8TH INTERNATIONAL CONFERENCE ON TRANSPORTATION AND TRAFFIC ENGINEERING (ICTTE 2019), 2020, 308