• DocumentCode
    2692799
  • Title

    A Modified Anti-Jamming Approach for Meter Band Radars

  • Author

    Dihua, Xu ; Jianwen, Chen ; Ya, Huang

  • Author_Institution
    Key Res. Lab, Wuhan Radar Acad.
  • fYear
    2006
  • fDate
    9-14 July 2006
  • Firstpage
    1429
  • Lastpage
    1432
  • Abstract
    A modified anti-jamming approach is presented for meter band uniform circular arrays (UCA) radar in the dense interference environment, which is based on least squares (LS) null constraints and the Gram-Schmidt orthogonalization (GSO) method for interference suppression, i.e., the LS-GSO approach for short. First, the LS null constraints with fast Fourier transform (FFT) approach is presented for radio frequency interference (RFI) suppression, especially the frequency modulation (FM) signals. Then, the GSO method is employed to eliminate the active interference. The simulation results indicate that the scheme is computationally efficient and robust in terms of interference suppression. A deep null can be formed to the direction of interference
  • Keywords
    fast Fourier transforms; interference suppression; jamming; least squares approximations; radar interference; FFT; Gram-Schmidt orthogonalization method; anti-jamming approach; dense interference; fast Fourier transform; frequency modulation signals; interference suppression; least squares null constraints; meter band uniform circular arrays radar; radio frequency interference suppression; Computational modeling; Fast Fourier transforms; Frequency modulation; Interference constraints; Interference elimination; Interference suppression; Least squares methods; RF signals; Radar; Radiofrequency interference; Beamforming; Meter band radars; Null constraints;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Antennas and Propagation Society International Symposium 2006, IEEE
  • Conference_Location
    Albuquerque, NM
  • Print_ISBN
    1-4244-0123-2
  • Type

    conf

  • DOI
    10.1109/APS.2006.1710818
  • Filename
    1710818