• DocumentCode
    2510467
  • Title

    Radar emitter classification using self-organising Neural Network models

  • Author

    Anjaneyulu, L. ; Murthy, N.S. ; Sarma, N. V S N

  • Author_Institution
    Dept. of ECE, Nat. Inst. of Technol., Warangal
  • fYear
    2008
  • fDate
    21-24 Nov. 2008
  • Firstpage
    431
  • Lastpage
    433
  • Abstract
    This paper presents a radar emitter identification and classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitterpsilas parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately identify and classify the emitter types.
  • Keywords
    direction-of-arrival estimation; fuzzy neural nets; radar computing; radar detection; self-organising feature maps; ARTMAP neural networks; Fuzzy ART; direction of arrival; radar emitter classification; self-organising neural network models; Artificial neural networks; Biological system modeling; Fuzzy neural networks; Neural networks; Pulse measurements; Radar detection; Radio frequency; Radiofrequency identification; Signal processing; Subspace constraints; ARTMAP; Artificial Neural Networks; EID; Fuzzy ART; Radar Emitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Recent Advances in Microwave Theory and Applications, 2008. MICROWAVE 2008. International Conference on
  • Conference_Location
    Jaipur
  • Print_ISBN
    978-1-4244-2690-4
  • Electronic_ISBN
    978-1-4244-2691-1
  • Type

    conf

  • DOI
    10.1109/AMTA.2008.4763033
  • Filename
    4763033