Model-based signal recovery : a geometric perspective
MetadataShow full item record
Model-based signal processing is concerned with measuring, understanding, and communicating data under the assumption that the (potentially high-dimensional) data in hand has in fact few degrees of freedom and can be accurately represented with a concise signal model. For instance, when the signal model is the class of sparse signals (i.e., signals with a concise representation in some basis), compressive sensing has proved effective in combining the sensing and compression stages, thereby allowing for more efficient sensors and powerful signal processing algorithms. A canonical result in ...