LAUSR

In this work, we apply the supervised descent method (SDM) to 3-D limited data inversion. In the measurement of the far field, the scattered field in 3-D measurement is small in magnitude and easily polluted by noise. Combining with limited observed data, the inversion problem is highly nonunique and ill-posed. To mitigate the ill-posedness, the model-based inversion is adopted by describing metallic targets based on prior information. Then, a series of generic descent directions is learned in the training stage iteratively using SDM. During inversion, the values of these model-based parameters are reconstructed directly using the learned directions. This approach is validated using both synthetic and experimental data. All simulations and experiments are conducted in a monostatic measurement setup to match real measurement conditions. The results show that by choosing proper prior information, the model-based SDM inversion can effectively compensate for the lack of data and achieve decent accuracy.