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Genetic algorithm-based mathematical morphology for clutter removal in airborne radars

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  • Seshagiri Duvvuri
  • Dyana Arumuganainar
  • Kamla Prasan Ray
  • Vengadarajan Alagarswami

Abstract

This paper presents a novel approach for clutter removal in airborne radars using a genetic algorithm and mathematical morphology. The clutter returns are detected when constant alarm rate processing is applied on range-Doppler images. In the proposed method, mathematical morphological operations are performed on range-Doppler images to obtain clutter images. The clutter image is then applied as a mask to remove false detections due to clutter. Also, the targets embedded in clutter are detected using gray-scale morphological operations. The morphological filter and the sequence of operations are designed by a genetic algorithm. The advantage of the proposed method is that it does not require the computation of statistical measures from clutter data and filters are optimally designed using a genetic algorithm. The proposed method has shown an increase in clutter leak reduction when compared to that of a deep morphological network.

Suggested Citation

  • Seshagiri Duvvuri & Dyana Arumuganainar & Kamla Prasan Ray & Vengadarajan Alagarswami, 2023. "Genetic algorithm-based mathematical morphology for clutter removal in airborne radars," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 37(3), pages 428-440, February.
    • Handle: RePEc:taf:tewaxx:v:37:y:2023:i:3:p:428-440
    DOI: 10.1080/09205071.2022.2145505
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