Compressed sensing is a novel theory of sampling and reconstruction that has emerged in the past several years. It seeks to leverage the inherent sparsity of natural images to reduce the number of necessary measurements to a sub-Nyquist level. We discuss how ideas from compressed sensing can benet ionospheric imaging in two ways. Compressed sensing suggests signal reconstruction techniques that take advantage of sparsity, oering us new ways of interpreting data, especially for undersampled problems. One example is radar imaging. We explain how compressed sensing can be used for radar imaging and show results that suggest improved performance over existing techniques. In addition to benetting the way we use data, compressed sensing can improve how we gather data, allowing us to shift complexity from sensing to reconstruction. One example is airglow imaging, wherein we propose replacing CC...

A novel technique for radar-imaging inversions is proposed which leverages ideas from the emerging field of compressed sensing. This new method takes advantage of the transform sparsity inherent in natural images. Theoretical recovery results are promising and are borne out by simulations in which this technique outperforms Capon’s method and the Maximum Entropy method. Preliminary results using data collected at the Jicamarca Radio Observatory are also presented.