Aspect invariant features for radar target recognition

In this study, the authors introduce a novel method of target feature extraction that in one form attempts to reduce variability inherent in target signatures and in a second form attempts to exploit the variability. It is well known that radar target signatures can be subject to significant fluctuations as a function of viewing geometry and that this behaviour severely degrades the performance of automatic target recognition algorithms. Here, they propose one variability reduction technique which groups strong scatterers in target signatures. A second method exploits variability by using the difference between two reduced resolution images to extract target angular stability information. In both cases, a grid cell structure enables extraction of these relatively stable features from synthetic aperture radar images with low computational complexity. Results, using measured target data taken from the MSTAR dataset, demonstrate that the proposed method of selecting feature vectors significantly improve the overall classification performance. A correct classification rate of 96% is reached in testing.