Adaptive Subaperture Imaging Algorithm for Multiaspect Synthetic Aperture Radar

A multiaspect synthetic aperture radar (SAR) is a novel imaging mode, which supports high spatial resolution, 3-D imaging, and automatic target recognition. However, traditional SAR imaging algorithms will not only degrade SAR images, but also lose significant angular information. Adaptive subaperture imaging algorithms can solve these problems. Distinguishing between anisotropic targets and isotropic targets and extracting the persistence angle are two main steps in these algorithms, both of which will be improved in this letter. First, a better characteristic coefficient, namely, amplitude statistical coefficient (ASC), is proposed to accurately distinguish between anisotropic targets and isotropic targets. Also, change-point detection is introduced to exactly extract the persistence angle. Besides, this letter attempts for the first time to quantitatively assess the characteristic coefficient indicating the anisotropy. For this purpose, two indicators are proposed: anisotropy discrimination index (ADI) and noise resistance range (NRR). The validity of this letter has been verified through the point target simulation data, the microwave anechoic chamber data, and the Gotcha public release data.