Compressed sensing, a method which relies on sparsity to reconstruct signals with relatively few measurements, has

the potential to greatly improve observation of distributed radar targets. We extend the theoretical work of others by

investigating the practical problems of implementing this approach for distributed targets, first examining a discrete

linear radar model suitable for compressed sensing and then discussing an example of this technique used on existing

data. Potential benefits include higher possible range resolution, complete filtering of noise without sidelobes or

artifacts, and the ability to identify different Doppler shifts within the same range window of a single pulse.