Supervised Descent Learning Technique for 2-D Microwave Imaging

In this communication, we study the application of the supervised descent method (SDM) for 2-D microwave imaging. SDM contains offline training and online prediction. In the offline stage, a training data set is generated according to prior information. Then, the average descent directions between a fixed initial model and the training models can be learned by iterative schemes. In the online stage, model reconstruction is achieved through iterations based on learned descent directions. This scheme offers a new perspective to incorporate prior information into inversion and reduce the computational complexity in the online inversion. Synthetic examples validate the accuracy and efficiency of this method.