Underwater object detection based on geophysical inversion information

Ying Weng, Meng Wu

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

Abstract

A geophysical inversion information based underwater object detection method is proposed by using the joint Gravity-Gradient and Magnetic-Gradient Inversion algorithms. The gravity-gradient and magnetic-gradient inversion equations are combined to estimate the orientation and distance of the underwater object. After calculating the relative positions of underwater object from the gravity-gradient inversion equations and magnetic-gradient inversion equations, the BP Neural Network is exploited to obtain an optimal geophysical inversion equation applied to underwater object detection. A typical three layered neural network of 6 input and 3 output neurons with a single hidden layer is constructed to realize information fusion. The leading characteristics of such neural network are strong parallel computing, learning and adaptive capabilities, as well as good fault-tolerance. With the proposed method, the trajectories of an underwater object can be detected accurately. Simulation results show that our method is more efficient than the joint gravity-gradient and magnetic-gradient inversion methods.

Original languageEnglish
Title of host publicationAutonomous Underwater Vehicles
Subtitle of host publicationDynamics, Developments and Risk Analysis
PublisherNova Science Publishers, Inc.
Pages1-24
Number of pages24
ISBN (Electronic)9781536118315
ISBN (Print)9781536118193
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Keywords

  • BP neural network
  • Gravity gradient inversion
  • Magnetic gradient inversion
  • Underwater object detection

ASJC Scopus subject areas

  • Engineering (all)
  • Computer Science (all)

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