Target Recognition in ISAR Image via Range Profile Perturbation Imaging

The deep learning-based target recognition methods have achieved great success in recent years. The major advantage lies in the ability to learn the high-level representation from the input data adaptively. Yet, they relied on large amounts of training data to cover the complete distribution space of samples. It is infeasible to be met in the practical applications. The fitting ability and the learning power were therefore deteriorated. To solve the problem, a new target augmentation method is proposed in this article. The original complex-valued image is first recast into the phase history. The range profiles are then randomly perturbed, such as the shift in cyclic, corruption in range bins, and the drop of range bins. The perturbed range profiles are used for target imaging, forming the new inverse synthetic aperture radar (ISAR) images. The diversity of the training dataset can be enhanced, and the learning effectiveness of the deep model can be improved accordingly. To verify the proposed method, several rounds of experiments are performed. The results demonstrate the advantages of the proposed method in comparison to state-of-the-art.