Compressive matched subspace detection

José L. Paredes, Zhongmin Wang, Gonzalo R. Arce, Brian M. Sadler

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.