3D Model-based Inversion with Limited Microwave Data Using Supervised Descent Method

In this work, we applied the Supervised Descent Method (SDM) to 3D model-based inversion with limited microwave data. The target shapes are described by parameters based on prior information. The values of these parameters are reconstructed iteratively using SDM in which the descent directions are learned from data set constructed based on prior information. The model-based inversion has less non-uniqueness compared with typical pixel-based inversion. Furthermore, sufficient prior information allows SDM to predict the parameter values with a good accuracy, even with data of limited view. This algorithm is validated using synthetic data. The results show that we can correctly recover target shape parameters with only mono-static reflection data.