GRNN-Based Predictors of UHF-Band Sea Clutter Reflectivity at Low Grazing Angle

As a basic characteristic of sea clutter, the reflectivity of sea surface depends on many factors. Various universal empirical models of low precision have been developed to predict the reflectivity of sea surface. In this letter, a method is proposed to train specific predictors by big data learning, where the universal empirical models are embedded to the architecture of the generalized regression neural network (GRNN) to enhance the learning ability and efficiency. On the sea clutter database measured by an island-based UHF-band radar in the offshore waters of the Yellow sea of China at low grazing angle, the GRNN-based predictors of different structures are compared with other predictors. The results on the database show that the GRNN-based predictors behave better at learning efficiency, prediction precision, and robustness.