Azimuthal constraint representation for synthetic aperture radar target recognition along with aspect estimation

In this paper, an azimuthal constraint representation is proposed for target recognition of synthetic aperture radar (SAR) images along with aspect angle estimation. Because of SAR azimuthal sensitivity, only those training images with similar azimuths to the test sample from the truly corresponding class are useful in the linear representation. Therefore, this study performs the linear representation in individual classes within a certain azimuth interval. The target label of the test sample is identified according to the minimum reconstruction error. Based on the best location of the azimuth interval, the aspect angle of the test sample can be simultaneously estimated using a linearly weighting algorithm. To demonstrate the validity of this method, experimental investigations are conducted on the stationary target acquisition and recognition (MSTAR) dataset under several operating conditions. In addition, the proposed method is simultaneously compared with several present SAR target recognition and aspect angle estimation methods.

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