Driven by the good classification performance of the convolutional neural network (CNN), this study proposes a CNN-based synthetic aperture radar (SAR) target recognition method. In this paper, a novel data augmentation algorithm is proposed via target reconstruction based on attributed scattering centers (ASC). The ASCs reflect the electromagnetic phenomenon of SAR targets, which can be used to reconstruct the target's characteristics. The sparse representation (SR) algorithm is first employed to extract the ASCs from a single SAR image. Afterward, some of the extracted ASCs are selected to reconstruct the target's image. By repeating the process, many new images can be generated as available training samples. In the classification stage, a CNN architecture is designed and trained by the augmented samples. For the test sample, it is also reconstructed using all the extracted ASCs thus relieving the interferences caused by the clutters or noises in the background. Finally, the reconstructed image from the test sample is classified based on the trained CNN. The reconstructed image from the ASCs can reduce the clutters and noises thus enhancing the image quality. More importantly, the generated new training samples could cover more operating conditions, which may probably occur in SAR target recognition. Therefore, the trained CNN can work more robustly in different situations. In the experiments, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to evaluate the performance of the proposed approach. This method could classify the 10 classes of targets with an accuracy of 99.48% under the standard operating condition (SOC). For the extended operating conditions like configuration variance, depression angle variance, noise corruption, and partial occlusion, the proposed method also displays superior performance over some baseline algorithms drawn from state-of-the-art literature. INDEX TERMS Convolutional neural network (CNN), synthetic aperture radar (SAR), target recognition, attributed scattering center (ASC), data augmentation.

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Abstract:The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which play critical roles in process design and optimization. In this investigation, the true stress-strain data of an Inconel 718 superalloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1153-1353 K and strain rate range of 0.01-10 s −1 on a Gleeble 3500 testing machine (DSI, St. Paul, DE, USA). Then the constitutive relationship was modeled by an optimally-constructed and well-trained back-propagation artificial neural network (ANN). The evaluation of the ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of Inconel 718 superalloy. Consequently, the developed ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the continuous mapping relationship for temperature, strain rate, strain and stress. Finally, the constructed ANN was implanted in a finite element solver though the interface of "URPFLO" subroutine to simulate the isothermal compression tests. The results show that the integration of finite element method with ANN model can significantly promote the accuracy improvement of numerical simulations for hot forming processes.

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