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Neural Networks for Improved Target Differentiation with Sonar

  • Naima Ait Oufroukh
  • Etienne Colle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

This study investigates the processing of sonar signals with neural networks for robust recognition of indoor robot environment composed of simple objects (plane, corner, edge and cylinder). The neural networks can differentiate more targets with higher accuracy. It achieves this by exploiting the identifying features extracted from sonar signals. In this paper we compare two different architectures of neural networks (global and specialized structure) in term of classification rates, the best classifier obtained is used to recognize a robot environment. The results strengthen our claims that sonar can be used as a viable system for object recognition in robotics and other application domains.

Keywords

Mobile Robot Layer Neuron Ultrasonic Sensor Hide Layer Neuron Quadratic Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Naima Ait Oufroukh
    • 1
  • Etienne Colle
    • 1
  1. 1.Laboratoire Systèmes ComplexesEvry CedexFrance

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