High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

Statistics and modeling is gaining more and more attention as the time for big data is coming. Variable choosing plays a significant role during modeling. The traditional methods like OLS and ridge regression could not satisfy interpretability and prediction accuracy at the same time. Tibshirani. R prompted Lasso and the new method could not only solve the above problem but also decrease the complexity of calculation. The paper aims to compare Lasso and adaptive lasso, elastic net. We do experiment on the classical case-diabetes patient data and choose model with AIC, BIC and cross validation to get the advantage and disadvantage of the above methods. According to the result, we draw the conclusion that Lasso performances better in variable selection, however, the predictions are not as accurate as the other two methods. Adaptive Lasso selects less variables and gives a more accurant prediction value. Elastic net has a most interpretative model.

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