Matched Subspace Detection Using Compressively Sampled Data

Abstract

We consider the problem of detecting whether a high dimensional signal lies in a given low dimensional subspace using only a few compressive measurements of it. By leveraging modern random matrix theory, we show that, even when we are short on information, a reliable detector can be constructed via a properly defined measure of energy of the signal outside the subspace.