Compressive sensing for high resolution radar imaging

In this paper we present some preliminary results on the application of Compressive Sensing (CS) to high resolution radar imaging. CS is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than what is required by the Shannon sampling theorem. This method has already found its way in a number of applications where the sampling rate or the acquisition time are prohibitive for real time applications, such as high resolution medical and optical imaging. Actual demonstrations of CS with experimental radar data are still very few, and therefore in this paper we apply CS to two-dimensional radar imaging with experimental data. The measurement setup contains a small number of corner reflectors which are illuminated using a stepped sequence of frequencies, over a range of aspect angles. The CS approach uses only a random selection of frequencies and angles, to reconstruct the two-dimensional image. The results obtained with CS are compared with the one achieved with conventional focusing (Matched Filter). The results show that the corner reflectors are properly reconstructed, with a significant reduction in the amount of measurement samples.