Optimal sensing matrix for sparse linear models

In this paper, we propose a method for designing the optimal sensing of measurements which can be characterized by a sparse linear model. The aim of the sensing operation is not only to reduce the amount of data to be processed but also to reject undesired signals (interferences). As a result, we reduce the computation time and the error for estimating the unknown parameters of the model, with respect to the uncompressed data. Using synthetic data, we analyze the performance of the proposed algorithm. Additionally, we use real radar data to show an application of the method.