In this paper, we first propose ten new discrimination features of SAR images in the moving and stationary target acquisition and recognition (MSTAR) database. The Ada_MCBoost algorithm is then proposed to classify multiclass SAR targets. In the new algorithm, we introduce a novel large-margin loss function to design a multiclass classifier directly instead of decomposing the multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method. Finally, experiments show that the new features are helpful for SAR targets discrimination; the new algorithm had better recognition performance than three other contrast methods.

To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy c-means clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods.