Evaluation of Radiometric Localization Techniques Based on the Intensity of the Received Signal

被引:0
作者
Alves, L. F. B. [1 ]
Silva, A. D. C. [1 ]
Nobrega, L. A. M. M. [1 ]
Xavier, G. V. R. [2 ]
机构
[1] Univ Fed Campina Grande, Elect Engn Dept, Campina Grande, Paraiba, Brazil
[2] Univ Fed Sergipe, Elect Engn Dept, Sao Cristovao, Brazil
来源
2024 8TH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION SYSTEMS, CIRCUITS AND TRANSDUCERS, INSCIT 2024 | 2024年
关键词
envelope detection; localization; partial discharges; PARTIAL DISCHARGE;
D O I
10.1109/INSCIT62583.2024.10693389
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
This paper presents an analysis of the efficacy of radiometric localization techniques for partial discharges sources, utilizing received signal strength. Tests in experimental setups with dimensions ranging from 2 x 2 m to 6 x 4 m with two different sources to emulate the phenomena were employed: a controlled source and an oil cell. In addition, the envelope detection was evaluated for this purpose. The techniques based on amplitude made the detection and localization of PD sources more practical and feasible. The location errors are around 0.25 m indicates that these techniques are promising and could help to reduce the costs associated with partial discharges monitoring processes.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Grid-Based RFID Localization Using Tag Read Count and Received Signal Strength
    Jeevarathnam, Nanda Gopal
    Uysal, Ismail
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [22] A Recurrent Learning Method Based on Received Signal Strength Analysis for Improving Wireless Sensor Localization
    Amr Tolba
    Zafer Al-Makhadmeh
    Circuits, Systems, and Signal Processing, 2020, 39 : 1019 - 1037
  • [23] Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization
    Yan, Jun
    Zhao, Lin
    Tang, Jian
    Chen, Yuwei
    Chen, Ruizhi
    Chen, Liang
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (03) : 2824 - 2829
  • [24] On Received-Signal-Strength Based Localization with Unknown Transmit Power and Path Loss Exponent
    Wang, Gang
    Chen, H.
    Li, Youming
    Jin, Ming
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2012, 1 (05) : 536 - 539
  • [25] A Received Signal Strength Based Indoor Localization Algorithm Using ELM Technique and Ridge Regression
    Feng, Zhiyue
    Cao, Yanhua
    Yan, Jun
    PROCEEDINGS OF 2019 IEEE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION AND COMMUNICATION TECHNOLOGY (ICEICT 2019), 2019, : 599 - 603
  • [26] Satellite Image and Received Signal-based Outdoor Localization using Deep Neural Networks
    Mukhtar, Hind
    Erol-Kantarci, Melike
    2021 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2021,
  • [27] A Recurrent Learning Method Based on Received Signal Strength Analysis for Improving Wireless Sensor Localization
    Tolba, Amr
    Al-Makhadmeh, Zafer
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2020, 39 (02) : 1019 - 1037
  • [28] Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network
    Xu, Shuqi
    Wang, Zhuping
    Zhang, Hao
    Ge, Shuzhi Sam
    SENSORS AND MATERIALS, 2019, 31 (06) : 2043 - 2060
  • [29] Machine learning techniques for received signal strength indicator prediction
    Azoulay, Rina
    Edery, Eliya
    Haddad, Yoram
    Rozenblit, Orit
    INTELLIGENT DATA ANALYSIS, 2023, 27 (04) : 1167 - 1184
  • [30] Statistical Positioning Quality Metrics for Common Received Signal Strength-Based Positioning Techniques
    Gunia, Marco
    Lu, Yibo
    Joram, Niko
    Ellinger, Frank
    IEEE SENSORS JOURNAL, 2019, 19 (23) : 11377 - 11395