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STVF: Spatial-Temporal Variational Filtering for Localization in Underwater Acoustic Sensor Networks.

Keyong Hu1, Zhongwei Sun2, Hanjiang Luo3

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China. hukeyongouc@163.com.

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This study introduces a new underwater acoustic sensor network localization method using variational filtering. The spatial-temporal variational filtering (STVF) method enhances accuracy and robustness in challenging underwater environments.

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Area of Science:

  • Underwater Acoustic Sensor Networks (UASNs)
  • Localization Algorithms
  • Signal Processing

Background:

  • Underwater environments present significant challenges for sensor node localization due to moving nodes and signal propagation issues.
  • Accurate localization is critical for various applications in UASNs.

Purpose of the Study:

  • To propose a novel localization method for UASNs that addresses the challenges of node mobility and dynamic environmental factors.
  • To improve localization accuracy and coverage in UASNs.

Main Methods:

  • A variational filtering technique is employed, utilizing spatial correlation and temporal dependency.
  • A state evolution model captures node mobility, and a measurement model accounts for acoustic speed dynamics and range noise.
  • The method involves a two-phase variational filtering (prediction and update) and an iterative localization scheme.

Main Results:

  • The proposed method, spatial-temporal variational filtering (STVF), demonstrates superior localization accuracy compared to the SLMP algorithm.
  • STVF maintains relatively high localization coverage.
  • The STVF method exhibits greater robustness to parameter setting variations than SLMP.

Conclusions:

  • The developed STVF method effectively enhances localization performance in UASNs.
  • The approach offers a robust solution for accurate and reliable node positioning in dynamic underwater settings.