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Spectrum sensing (SS) in cognitive radio (CR) systems is of paramountimportance to approach the capacity limits for the Secondary Users (SU), whileensuring the undisturbed transmission of Primary Users (PU). In this paper, weformulate a cognitive radio (CR)systems spectrum sensing (SS) problem in whichSecondary Users (SU), with multiple receive antennae, sense a channel sharedamong multiple asynchronous Primary Users (PU) transmitting Multiple InputMultiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM)signals. The method we propose to estimate the opportunities available to theSUs combines advances in array processing and compressed channel sensing, andleverages on both the so called "shrinkage method" as well as on anover-complete basis expansion of the PUs interference covariance matrix todetect the occupied and idle angles of arrivals and subcarriers. The covariance"shrinkage" step and the sparse modeling step that follows, allow to resolveambiguities that arise when the observations are scarce, reducing the sensingcost for the SU, thereby increasing its spectrum exploitation capabilitiescompared to competing sensing methods. Simulations corroborate that exploitingthe sparse representation of the covariance matrix in CR sensing resolves thespatial and frequency spectrum of the sources.

Spatial-Spectral Sensing using the Shrink & Match Algorithm in Asynchronous MIMO OFDM Signals