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Classification of radar clutter using neural networks.

S Haykin1, C Deng

  • 1Commun. Res. Lab., McMaster Univ., Hamilton, Ont.

IEEE Transactions on Neural Networks
|January 1, 1991
PubMed
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This study introduces a neural network classifier for radar returns, achieving 89% accuracy in distinguishing weather, birds, and aircraft. This advanced radar classification method offers improved detection capabilities.

Area of Science:

  • Radar Signal Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Distinguishing between various radar returns (weather, birds, aircraft) is crucial for accurate environmental and air traffic monitoring.
  • Traditional classification methods may struggle with the complexity and variability of radar data.

Purpose of the Study:

  • To develop and evaluate a novel neural network-based classifier for radar return identification.
  • To compare the performance of the proposed neural network classifier against a traditional Bayes classifier.

Main Methods:

  • A multilayer feedforward network utilizing the back-propagation algorithm was designed.
  • Preprocessing and postprocessing procedures were integrated into the classifier architecture.
  • Feature selection, learning algorithm development, and implementation were performed.

Related Experiment Videos

  • The classifier was simulated on a Warp systolic computer for performance evaluation.

    • Main Results:

    • The neural network classifier achieved an average classification accuracy of 89% for generalization on single-scan radar data.
    • The system demonstrated effectiveness in distinguishing between weather, birds, and aircraft returns.
    • Comparative evaluation showed the multilayer neural network's performance against a Bayes classifier.

    Conclusions:

    • The developed neural network classifier offers a robust and accurate method for radar return classification.
    • The integration of preprocessing and postprocessing enhances the classifier's performance.
    • This approach shows promise for improving radar data analysis in various applications.