Editor´s comments

Compressed sensing is a field that has generated a lot of interest in recent years. It has also generated a lot of confusion. The feature article by Marco Donald Migliore should significantly help with that. It is a tutorial on compressed sensing and the companion field of sparse recovery, applied to applications in antenna measurements. The article beings with a simple introduction to the concept that in many real-world situations, measurements are made where many of the measurements in a set of data are zero or close to it. As a result, the measurement set can be substantially compressed with little loss of information. There is a simple mathematical basis for this associated with underdetermined linear systems, and that is the starting point for the author´s introduction to compressed sensing. He shows how compressed sensing problems can be formulated in terms of matrix problems in which only portions of the matrix contribute significantly to a useful reconstruction of the original data. He then looks at the various ways of applying minimization techniques, using different norms, to obtain the most meaningful reconstructions from a given set of data. Once the bases of these techniques have been developed, they are applied to example problems. These include the identification of faulty elements in an antenna array from a limited number of measurements of the far field of the array, and the behavior and optimization of the various solution approaches in the presence of noise. I found this to be a very clear and readable introduction to this interesting and useful topic.