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This paper considers a sensor network deployed in a fixed manner in water, which is capable of continuously measuring wake field An exponential-normal decay model is employed to describe the distribution of the wake field, and a linear approximation model is used as the spatial mean function. Bayesian kriging interpolation is applied to the observations of the sensors in the wake field to estimate the spatial data gradient. The gradient ascent method is utilized to estimate the source of the wake field, and an angle-based maximum likelihood estimation method is proposed to fuse the observations from various nodes in the network to estimate the trajectory of the underwater moving target. In the experiments, a sensor network is used to measure the wake field, and the feasibility of the method is validated through real data experiment.

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Trajectory Estimation Method for Underwater Moving Target Based on the Spatial Data Gradient from a Sensor Network

  • 会议论文

  • 发表日期：

    2025年

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  • Taili Du,
  • Gengchen Xu,
  • Peng Xu,
  • Hao Jin,
  • Bo Liu,
  • Xinyue Zhou,
  • Bowen Dong,
  • Minyi Xu

摘要

This paper considers a sensor network deployed in a fixed manner in water, which is capable of continuously measuring wake field An exponential-normal decay model is employed to describe the distribution of the wake field, and a linear approximation model is used as the spatial mean function. Bayesian kriging interpolation is applied to the observations of the sensors in the wake field to estimate the spatial data gradient. The gradient ascent method is utilized to estimate the source of the wake field, and an angle-based maximum likelihood estimation method is proposed to fuse the observations from various nodes in the network to estimate the trajectory of the underwater moving target. In the experiments, a sensor network is used to measure the wake field, and the feasibility of the method is validated through real data experiment.