SAR images contain a large amount of noise, and related algorithms will cause high complexity when increasing the accuracy. To overcome this problem, a neural network model based on the attention mechanism was proposed in this paper. The model extracted information in two stages. It gradually extracts high-level features by reducing noise first and then adding hybrid attention. First, use dual-channel one-dimensional convolution to reconstruct the residual shrinkage network to construct a lightweight and efficient feature module, which improved the information extraction of the module with the consumption of a small amount of computing resources. Then, it was used as the backbone for model construction. Subsequently, mixed adaptive pooling was adopted to improve the maximum pooling. After that, dimensionality was reduced by pooling and linear interpolation was used to increase dimensionality, so as to generate feature weights of mixed dimension. Tests were performed on MSTAR dataset. The results showed that compared with the advanced algorithms, the proposed model in this paper can greatly reduce the amount of parameters and complexity while ensuring accuracy. The robustness test demonstrated that the model can effectively identify images with noise being added.

Absrtact: With the increasing complexity, information and intelligence of combat systems and weapons equipment, the traditional fault diagnosis technology can not meet the requirements of rapid and accurate fault diagnosis of equipment. In this paper, according to the fault characteristics and maintenance status of a certain type of equipment, combined with case-based reasoning technology, an equipment maintenance system which can realize intelligent query, case accumulation and fault reasoning is proposed. Finally, the feasibility of the method is proved by an example.