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Statistical Classifier of Radar Returns

  • Miroslava Ivanova
  • Boriana Vassileva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2542)

Abstract

For the last years many algorithms for radar return identification based on wideband or imaging radar have been developed. However, because of its all-weather performance, microwave radar is still the most reliable sensor for surveillance.

This paper considers the problem of radar return identification using microwave radar. Radar return is classified into several different classes, namely ground, weather, birds and aircraft, using Stehwien-Haykin classifier based on features derived from maximum entropy spectral analysis. To evaluate the practicality and effectiveness of this classifier its performance is tested by Monte Carlo simulation. Clutter samples, are generated by passing a white noise sequence through linear digital filter. The power spectral density is reasonably represented by second or third order Batterworth. The results show that the classifier correctly identifies the radar return with mean error rate less than 4 %.

Keywords

Power Spectral Density Microwave Radar Radar Return Reliable Sensor White Noise Sequence 
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 2003

Authors and Affiliations

  • Miroslava Ivanova
    • 1
  • Boriana Vassileva
    • 1
  1. 1.Central Laboratory for Parallel ProcessingBulgarian Academy of SciencesSofiaBulgaria

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