Compressive matched subspace detection

Abstract

Matched subspace detectors based on the framework of Compressive Sensing (CS) are developed. The proposed approach, called compressive matched subspace detectors, exploits the sparsity model of the signal-of-interest in the design of the random projection operator. By tailoring the CS measurement matrix (projection operator) to the subspace where the signal-of-interest is known to lie, the compressive matched subspace detectors can effectively capture the signal energy while the interference and noise effects are mitigated at sub-Nyquist rate. The proposed detection approach is particularly suitable for detection of wideband signals that emerge in modern communication systems that demand high-speed ADCs. The performance of the subspace compressive detectors are studied by analytically deriving closed-form expressions for the detection probability and through extensive simulations.