Super-resolution ISAR imaging using polarimetric techniques for subspace dimensionality

This paper demonstrates a technique for super-resolution ISAR imaging using polarimetry to determine the signal subspace dimensionality for the MUSIC algorithm. By creating a feature vector, polarimetric information from the target is used to accurately determine the number of primary scatterers in a simple target geometry with added noise. This information is used to determine the signal subspace dimension, which is used in the MUSIC super-resolution imaging algorithm, resulting in high resolution target images in the presence of additive noise.