ENHANCED RADAR IMAGING VIA SPARSITY REGULARIZED 2D LINEAR PREDICTION


Erer I. , SARIKAYA K., BOZKURT H.

22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portekiz, 1 - 05 Eylül 2014, ss.1751-1755 identifier identifier

  • Basıldığı Şehir: Lisbon
  • Basıldığı Ülke: Portekiz
  • Sayfa Sayıları: ss.1751-1755

Özet

ISAR imaging based on the 2D linear prediction uses the 12 norm minimization of the prediction error to obtain 2D autoregressive (AR) model coefficients. However, this approach causes many spurious peaks in the resulting image. In this study, a new ISAR imaging method based on the 2D sparse AR modeling of backscattered data is proposed. The 2D model coefficients are obtained by the 12-norm minimization of the prediction error penalized by the 11 norm of the prediction coefficient vector. The resulting 2D prediction coefficient vector is sparse, and its use yields radar images with reduced side lobes compared to the classical 12-norm minimization.