Abstract:

Under the assumption that the target scene is sparse, the compressive sensing (CS) imaging can use very few data to obtain good images with high contrast and no side-lobe interference as compared with the conventional range-Doppler imaging methods. In this paper, an inverse synthetic aperture radar (ISAR) CS imaging method based on the minimization of the null-space l₁ norm is proposed. The solution of the underdetermined linear imaging system is decomposed into two parts: the preliminary value and the residual value. First, the weighted lease square method is used to estimate the preliminary value, which is used as the target initial image. Then, the l₁ norm of the target scene reflectivity is introduced as an additional non-linear measurement and used in the image reconstruction. Within the Kalman filter framework, the residual value in the null space is estimated by minimizing the l₁ norm of the target scene using the nonlinear pseudo-measurement. The ISAR real data processing verifies the effectiveness of the proposed method. The image quality obtained by the proposed method is better than that of the orthogonal matching pursuit algorithm (OMP) and the primal-dual l₁ norm minimization method. The imaging time is much less than the primal-dual l₁ norm minimization method and comparable to OMP.

Key words: inverse synthetic aperture radar (ISAR), imaging, compressive sensing (CS), l₁ norm, null space