Transferring Synthetic Elementary Learning Tasks to Classification of Complex Targets

Deep learning has a promising impact on target classification performance at the expense of huge training data requirements. Therefore, the use of simulated data is inevitable for convergence of deep models (DMs). However, generating synthetic data for real-life complex targets can be quite tedious and is not always possible. In this study, DMs trained with synthetic one-dimensional scattered data of elementary targets are transferred to classify complex targets from measured signals for the first time. For this purpose, a novel system is proposed by combining three strategies: first, initial training of DMs using analytical and simulated time domain scattered data obtained from the basic targets; second, the last layers of initial DMs are fine-tuned by transfer learning using measured signals of the real targets; and third, an ensemble model is developed to generate a model that can completely represent real target characteristics by combining diverse and complementary properties of the fine-tuned DMs. The proposed system provides higher accuracy, sensitivity, and specificity performances compared to the existing methods.