Random matrix theory and performance prediction of subspace methods

Abstract

Subspace methods constitute a powerful class of techniques for detection, estimation and classification of signals buried in noise. Recent results in random matrix theory precisely quantify the accuracy of subspaces estimates from finite, noisy data in both the white noise and colored noise setting. This advance facilitates unified performance analysis of signal processing methods that rely on these empirical subspaces. We discuss the pertinent theory and its application to the characterization of the performance of direction-of-arrival estimation, matched subspace detection and subspace clustering for large arrays in the sample-starved setting for both white and colored noise.