L(1/2) Regularization for ISAR Imaging and Target Enhancement of Complex Image

Synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging technology are powerful tools to acquire high-resolution radar image, which is an important basis for further automatic target recognition (ATR). For ISAR, if the radar frequency is high enough, signals that the radar received usually have strong sparsity when the radar data convert to Fourier domain, and they can be downsampled and restored by compressed sensing (CS). As for the regularization method in CS theory, while
$L_{1}$
regularization is popular, the
$L_{q}(0 < q < 1)$
regularization is proved to be a sparse regularization framework and could achieve well performance. In this work, inspired by the
$L_{1}$
-based complex approximated message passing (CAMP) method and the
$L_{(1/2)}$
regularization framework, we extend the CAMP method into an
$L_{(1/2)}$
-based iterative thresholding method in ISAR imaging under the downsampling rate of the scattered fields. The matched filtering (MF) method is widely used to generate the SAR image, where targets are usually overwhelmed by noise and scene background. In order to make the target enhanced to further improve recognition, regularization is adopted in recovering sparse solution and the clutter reduction of MF image. This work implements the
$L_{({1}/{2})}$
-based regularization into the SAR image target enhancement and the scene-noise reduction via CAMP. The SAR image is generated by ideal point scatterers and the real measurement data of RADARSAT-1. Given the sparsity estimated, the experiment results show the advantage of
$L_{({1}/{2})}$
regularization compared with the
$L_{1}$
regularization.