Autonomous Sonar Classification Using Expert Systems

Abstract

An expert system can process active sonar returns, perform geometric analysis and autonomously classify detected underwater objects. Autonomous classification of objects is an essential requirement for independent operation by autonomous underwater vehicles (AUVs). Most AUVs are only capable of rudimentary sensor analysis, since standard approaches to evaluation and classification of sonar data require excessive signal processing and computational power to be practical. This paper describes how to develop an autonomous sonar classification expert system for a working AUV. A fundamental approach is presented for applying geometric reasoning and expert system heuristics to sonar classification. Preliminary sonar processing is performed using parametric regression line fitting. A polyhedron-building algorithm correlates the parametric regression line segments into geometric objects. After quantifying geometric object attributes, objects are classified using rule-based evaluation of quantitative and qualitative attributes combined with sonar classification heuristics.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1992
Accession Number
ADA280589

Entities

People

  • Donald P. Brutzman
  • Mark A. Compton
  • Yutaka Kanayama

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Systems
  • Autonomous Underwater Vehicles
  • Autonomous Vehicles
  • Collision Avoidance Systems
  • Computer Vision
  • Control Systems
  • Divers
  • Expert Systems
  • Geometric Forms
  • Regression Analysis
  • Signal Processing
  • Simulators
  • Three Dimensional
  • Underwater Vehicles
  • Unmanned Systems
  • Unmanned Underwater Vehicles

Fields of Study

  • Engineering

Readers

  • Artificial Intelligence
  • Computer Vision.
  • Robotics and Automation.