Target Localization and Reconstruction Using Compressive Sampling

In this paper we propose a method to detect and reconstruct the image of objects by solving the inverse scattering problem using compressive sampling. This work is an extension of previous research where the authors considered the localization and reconstruction of dot targets and simple targets. Unlike the latter, now we deal with more complex objects of two dimensions which can be seen as formed by multiple dots or simple targets. Several objects of different characteristics were studied using a detection and reconstruction model based on convex optimization. The model was evaluated under different configurations and conditions looking for limiting operating conditions. In addition, a threshold method is implemented to improve the recovered images and three error indicators were defined to measure the error in a given reconstructed image: global error, estimation error and reconstruction error.