A SAR-ATR method with favorable performance on translational invariance is proposed in this paper. Nowadays, supervised learning is the main way to realize image processing and target detection, but performance of most trained models will be greatly influenced by distribution of targets in training samples. In some recent researches of SAR-ATR, models trained on MSTAR dataset apply methods of data augmentation to increase randomness of target position in training samples, so that the models could have a certain capability of translational invariance. The method that we proposed based on convolutional neural network (CNN) doesn't need operation of data augmentation at all, and the comparison experiments based on manually shifted target slices proved that our model performed better in detecting and recognizing shifted targets. Experiments show that the proposed method can accurately locate position of different kinds of shifted targets and realize detection and recognition correctly.