Performance of the MACH filter and DCCF algorithms on the 10-class public release MSTAR data set

摘要

The maximum average correlation height (MACH) filter and distance classifier correlation filter (DCCF) correlation algorithms are evaluated using the 10 class publicly released MSTAR database. The successful performance of these algorithms on a 3-class problem has been previously reported. The algorithms are optimized by design to be robust to variations (distortions) in the target's signature as well as discriminate between classes. Unlike Matched Filtering (or other template based methods), the proposed approach requires relatively few filters. The paper reviews the theory of the algorithm, key practical advantages and details of test results on the 10-class public MSTAR database.