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Sonar Data Classification Using a New Algorithm Inspired from Black Holes Phenomenon

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  • Mohamed Elhadi Rahmani

    (GeCoDe Laboratory, Dr. Tahar Moulay University of Saida, Saida, Algeria)

  • Abdelmalek Amine

    (GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saida, Algeria)

  • Reda Mohamed Hamou

    (GeCoDe Laboratory, Department of computer Science, Tahar Moulay University of Saida, Algeria)

Abstract

Sound Navigation and Ranging (Sonar) is underwater sound detection used in boats or submarines to navigate, communicate with or detect objects under the surface of water based on sound propagation. It is helpful for exploring and mapping the ocean because sound waves travel farther in the water than do radar and light waves. Based on signal data obtained from sonar, this article presents a new heuristic approach inspired from black holes' phenomenon proposed by Schwarzschild, it has been applied to the classification sonar returns from two undersea targets, a metal cylinder and a similarly-shaped rock. Results are very satisfied (almost 83% of accuracy) compared to original works. in manner that encourage to keep working on paper, the main idea of this article is to benefit from the power of nature to solve complex problems in computer science

Suggested Citation

  • Mohamed Elhadi Rahmani & Abdelmalek Amine & Reda Mohamed Hamou, 2018. "Sonar Data Classification Using a New Algorithm Inspired from Black Holes Phenomenon," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 8(2), pages 25-39, April.
    • Handle: RePEc:igg:jirr00:v:8:y:2018:i:2:p:25-39
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