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      Neural networks for improved target differentiation and localization with sonar

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      Author(s)
      Ayrulu, B.
      Barshan, B.
      Date
      2001
      Source Title
      Neural Networks
      Print ISSN
      0893-6080
      Publisher
      Pergamon Press
      Volume
      14
      Issue
      3
      Pages
      355 - 373
      Language
      English
      Type
      Article
      Item Usage Stats
      199
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      233
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      Abstract
      This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. Copyright © 2001 Elsevier Science Ltd.
      Keywords
      Acoustic signal processing
      Artificial neural networks
      Discrete wavelet transform
      Feature extraction
      Learning
      Sonar sensing
      Target differentiation
      Target localization
      Artificial intelligence
      Backpropagation
      Computer vision
      Convergence of numerical methods
      Data reduction
      Learning algorithms
      Learning systems
      Robotics
      Generating-shrinking algorithms
      Neural networks
      Algorithm
      Echolocation
      Information processing
      Pattern recognition
      Robotics
      Sensor
      Signal processing
      Visual aid
      Sound Localization
      Permalink
      http://hdl.handle.net/11693/24876
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/S0893-6080(01)00017-X
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      • Department of Electrical and Electronics Engineering 3868
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