A TDOA Method for Underwater Target Location Based on Particle Swarm Optimization with Experiment Verification

被引：0
|
作者：
Zhou, Zihao [1 ]
Shi, Yang [1 ]
Yang, Long [1 ]
Zhang, Ruolan [1 ]
机构：
[1] Northwestern Polytech Univ, Shaanxi Key Lab Underwater Informat Technol, Sch Marine Sci & Technol, Xian, Peoples R China
来源：
OCEANS 2024 - SINGAPORE | 2024年
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
time difference of arrival; particle swarm optimization; experiment verification;
D O I：
10.1109/OCEANS51537.2024.10682172
中图分类号：
P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
TDOA methods play a significant role in passive target location. When using the time difference of arrival (TDOA) method to passively locate underwater targets, measurement errors can inevitably affect the accuracy of the location. This paper proposes a TDOA method based on particle swarm optimization (PSO) for locating underwater targets using distributed multi-agent systems. The simulation results demonstrate the method's effectiveness. An experiment was conducted to verify the method at sea in Sanya Bay in the South China Sea. The method's performance was verified at sea, demonstrating an average location error of 7.41 m. Compared to TDOA-Taylor, the method exhibits a 9.74% reduction in location error.
引用
收藏
页数：5
相关论文
共 50 条
  • [21] Particle Swarm Optimization Based Method for Fabric Evaluation
    Bao, A-Sai
    Gu, Quan
    Hou, Cai-Hong
    TEXTILE BIOENGINEERING AND INFORMATICS SYMPOSIUM PROCEEDINGS, VOLS 1-3, 2013, : 1025 - 1030
  • [22] Numerical Integration Method Based on Particle Swarm Optimization
    Djerou, Leila
    Khelil, Naceur
    Batouche, Mohamed
    ADVANCES IN SWARM INTELLIGENCE, PT I, 2011, 6728 : 221 - 226
  • [23] An Improved Particle Swarm Optimization Method Based on Chaos
    Yang, Zuyuan
    Yang, Huafen
    Yang, You
    Zhang, Lihui
    2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC), 2014, : 209 - 213
  • [24] Method of particle swarm optimization based on the chaos map
    Liu D.-H.
    Yuan S.-C.
    Lan Y.
    Ma X.-J.
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2010, 37 (04): : 764 - 769
  • [25] Integrating Particle Swarm Optimization with Stochastic Point Location Method in Noisy Environment
    Zhang, JunQi
    Lu, SiYu
    Zang, Di
    Zhou, MengChu
    2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2016, : 2067 - 2072
  • [26] Research on Node Location Technology Based on Improved Particle Swarm Optimization
    Cai, Xiumei
    Hu, Kexin
    Zhang, Fangjuan
    Li, Wei
    Li, Yiran
    Zhang, Zichao
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 2833 - 2837
  • [27] Location Selection for Regional Logistics Center Based on Particle Swarm Optimization
    Huang, Yingyi
    Wang, Xinyu
    Chen, Hongyan
    SUSTAINABILITY, 2022, 14 (24)
  • [28] Multiple target passive location of TDOA based on bidirectional elect and nearest neighbor method
    Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai
    264001, China
    Yuhang Xuebao, 4 (483-488): : 483 - 488
  • [29] Evaluation of a Particle Swarm Optimization Based Method for Optimal Location of Photovoltaic Grid-connected Systems
    Gomez, M.
    Jurado, F.
    Diaz, P.
    Ruiz-Reyes, N.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2010, 38 (10) : 1123 - 1138
  • [30] New energy vehicle charging station location method based on improved particle swarm optimization algorithm
    Zhang, Liang-Li
    Ma, Xiao-Feng
    Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2024, 54 (08): : 2275 - 2281
12345