Underwater object detection based on geophysical inversion information

Ying Weng, Meng Wu

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


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.
Number of pages24
ISBN (Electronic)9781536118315
ISBN (Print)9781536118193
Publication statusPublished - 1 Jan 2017
Externally publishedYes


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

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science


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