• DocumentCode
    3075989
  • Title

    Principal component analysis for classifying passive sonar signals

  • Author

    Soares-Filho, William ; De Seixas, Jose Manoel ; Pereira Caloba, L.

  • Author_Institution
    IPqM, Brazilian Navy Res. Inst., Rio de Janeiro, Brazil
  • Volume
    3
  • fYear
    2001
  • fDate
    6-9 May 2001
  • Firstpage
    592
  • Abstract
    Principal component analysis in the frequency domain is used for neural identification of the radiated noise from ships. For comparison, components are extracted from three different approaches: linear (PCA) and nonlinear (NLPCA) principal component analysis, and neural discriminating analysis (NDA). The classifier using NDA achieves a classification efficiency of about 93% using only 3 components, while the classifiers using PCA and NLPCA need up to 33 components to reach the same efficiency
  • Keywords
    neural nets; principal component analysis; signal classification; sonar signal processing; neural discriminating analysis; neural identification; passive sonar signals; principal component analysis; sonar signal classification; Acoustic noise; Acoustic sensors; Frequency domain analysis; Machinery; Marine vehicles; Narrowband; Principal component analysis; Sensor arrays; Signal processing; Sonar equipment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    0-7803-6685-9
  • Type

    conf

  • DOI
    10.1109/ISCAS.2001.921380
  • Filename
    921380
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3075989