• DocumentCode
    643821
  • Title

    DOA estimation for closely spaced signals

  • Author

    Jian Liu ; Ai-min Song ; Feng Yang ; Xing-yang Guo ; Jiao Guan

  • Author_Institution
    Sch. of Inf. & Navig., Air Force Eng. Univ., Xi´an, China
  • fYear
    2013
  • fDate
    5-8 Aug. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    High resolution direction of arrival (DOA) estimation algorithms based on the subspace decomposition received considerable attention while rarely used in the practical applications. One of the reasons is its difficulty to resolve closely spaced signals in low signal-to-noise ratio (SNR). For applications of closely spaced signals within a priori known angle range, we filter the spectrum in direction domain to improve the SNRs of signals on array sensors and reconstruct the covariance matrix with which MUSIC algorithm is applied. The improvement in the aspects of resolution and accuracy in low SNR is shown by Monte-Carlo simulations.
  • Keywords
    Monte Carlo methods; covariance matrices; direction-of-arrival estimation; signal classification; signal reconstruction; signal resolution; MUSIC algorithm; Monte-Carlo simulation; SNR; array sensor signal; closely-spaced signals; covariance matrix reconstruction; direction domain; direction-of-arrival estimation algorithm; high-resolution DOA estimation algorithm; priori known angle range; signal-to-noise ratio; subspace decomposition; Arrays; Direction-of-arrival estimation; Multiple signal classification; Sensors; Signal processing algorithms; Signal resolution; Signal to noise ratio; Array signal processing; MUSIC; direction-domain filtering; direction-of-arrival (DOA) estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on
  • Conference_Location
    KunMing
  • Type

    conf

  • DOI
    10.1109/ICSPCC.2013.6664142
  • Filename
    6664142