Automatic recognition of MSTAR targets using radar shadow and superresolution features

Automatic target recognition from high range resolution radar profiles remains an important and challenging problem. In this paper, we present a novel feature set for this task that combines a representation of the target's radar shadow with a noise-robust superresolution characterisation of the target scattering centres derived from the MUSIC algorithm. Using an HMM to represent aspect dependence, we demonstrate that the inclusion of the shadow features results in a significant improvement in recognition performance. We evaluate our proposed feature set on a closed-set identification task using targets from the MSTAR database and show that it results in lower recognition error rates than previously published methods using the same data.