skip to main content
10.1145/3631726.3631736acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
short-paper

Deep Learning for TDOA-Based Underwater Target Localization Considering Stratification Effect

Published: 12 June 2024 Publication History

Abstract

This paper explores an underwater localization system aimed at addressing the challenge of accurately locating a target node in an inhomogeneous underwater environment. In this system, multiple receivers with known positions collaborate to enhance the localization process. The presence of the stratification effect causes underwater acoustic signals to propagate along a curved path, introducing non-Gaussian noise and leading to localization errors. Traditional localization algorithms, such as the weighted least squares algorithm, are designed for ideal underwater environment with low-Gaussian noise, assuming the signal is transmitted in a straight line. However, these algorithms are limited in their applicability. To overcome this limitation, we put forth a deep learning-based underwater range difference correction network (DL-URDCN). This innovative approach effectively removes the non-Gaussian noise in range difference, converting it into low-Gaussian noise. Furthermore, an iterative weighted least squares method based on Taylor series expansion (TIWLS) algorithm for the time-difference-of-arrival (TDOA) is utilized to solve a localization problem. Simulation results demonstrate that the DL-URDCN-TIWLS algorithm outperforms existing benchmarks in terms of localization performance and satisfies the Cramér Rao Lower Bound (CRLB).

References

[1]
Harold C. Burger, Christian J. Schuler, and Stefan Harmeling. 2012. Image denoising: Can plain neural networks compete with BM3D?. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. 2392–2399.
[2]
Xiansheng Guo, Nirwan Ansari, Lin Li, and Huiyong Li. 2018. Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests. IEEE Internet of Things Journal 5, 6 (Dec. 2018), 4686–4698.
[3]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770–778.
[4]
Bin Liu, Hongyang Chen, Ziguo Zhong, and H. Vincent Poor. 2010. Asymmetrical Round Trip Based Synchronization-Free Localization in Large-Scale Underwater Sensor Networks. IEEE Transactions on Wireless Communications 9, 11 (2010), 3532–3542.
[5]
Xiaojun Mei, Dezhi Han, Nasir Saeed, Huafeng Wu, Teng Ma, and Jiangfeng Xian. 2022. Range Difference-Based Target Localization Under Stratification Effect and NLOS Bias in UWSNs. IEEE Wireless Communications Letters 11, 10 (Oct. 2022), 2080–2084.
[6]
Eliyeh Mortazavi, Reza Javidan, Mohammad Javad Dehghani, and Vali Kavoosi. 2017. A robust method for underwater wireless sensor joint localization and synchronization. Ocean Engineering 137 (2017), 276–286.
[7]
Yi Qu, Jie Sun, Yu Tian, and Jiancheng Yu. 2022. 3D Deep Residual Convolutional Neural Network for Underwater Acoustic Source Localization Using Local Acoustic Intensity Field. In OCEANS 2022 - Chennai. 1–5.
[8]
Hamid Ramezani, Hadi Jamali-Rad, and Geert Leus. 2013. Target Localization and Tracking for an Isogradient Sound Speed Profile. IEEE Transactions on Signal Processing 61, 6 (Mar. 2013), 1434–1446.
[9]
Ling Wang, Xiaohong Shen, Xi Liu, Fei Hua, and Haiyan Wang. 2019. Target localization based on weighted total least squares in underwater acoustic networks. In 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). 1–5.
[10]
Chi Wu, Hongwei Hou, Wenjin Wang, Qing Huang, and Xiqi Gao. 2018. TDOA Based Indoor Positioning with NLOS Identification by Machine Learning. In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). 1–6.
[11]
Jing Yan, Ziqiang Xu, Xiaoyuan Luo, Cailian Chen, and Xinping Guan. 2019. Feedback-Based Target Localization in Underwater Sensor Networks: A Multisensor Fusion Approach. IEEE Transactions on Signal and Information Processing over Networks 5, 1 (2019), 168–180.
[12]
Jihao Yin, Qun Wan, Shiwen Yang, and K. C. Ho. 2016. A Simple and Accurate TDOA-AOA Localization Method Using Two Stations. IEEE Signal Processing Letters 23, 1 (Jan. 2016), 144–148.
[13]
Xudong You, Zefang Lv, Yuzhen Ding, Wei Su, and Liang Xiao. 2020. Reinforcement Learning Based Energy Efficient Underwater Localization. In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). 927–932.
[14]
Bingbing Zhang, Hongyi Wang, Tao Xu, Liming Zheng, and Qing Yang. 2016. Received signal strength-based underwater acoustic localization considering stratification effect. (Jun. 2016), 1–8.
[15]
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing 26, 7 (Jul. 2017), 3142–3155.
[16]
Liang Zhang, Tao Zhang, Hyo-Sang Shin, and Xiang Xu. 2021. Efficient Underwater Acoustical Localization Method Based On Time Difference and Bearing Measurements. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1–16.
[17]
Yanbin Zou and Huaping Liu. 2020. TDOA Localization With Unknown Signal Propagation Speed and Sensor Position Errors. IEEE Communications Letters 24, 5 (May 2020), 1024–1027.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
WUWNet '23: Proceedings of the 17th International Conference on Underwater Networks & Systems
November 2023
239 pages
ISBN:9798400716744
DOI:10.1145/3631726
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Underwater localization
  2. deep learning.
  3. stratification effect

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

WUWNet 2023

Acceptance Rates

Overall Acceptance Rate 84 of 180 submissions, 47%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 28
    Total Downloads
  • Downloads (Last 12 months)28
  • Downloads (Last 6 weeks)4
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media