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Dynamic Spectrum Access (DSA) is receiving considerable interest as a means to improve spectral usage in licensed bands. In order to avoid interference to licensed users, spectrum sensing has emerged as an enabling technology for DSA. The requirements for spectrum sensors are stringent, as licensed user detection must be performed reliably at low signal-to-noise ratios (SNR). Sensing performance can be improved by exploiting signal features not present in the background noise. These approaches result in tradeoffs among performance and robustness to departures from the signal model. We consider second-order signal features and develop detectors exploiting spatial (by using multiple antennas) as well as temporal signal correlation, taking advantage of the fact that the power spectrum of the primary signal at each antenna can be known up to a complex scalar representing the unknown propagation channel. A low-SNR Generalized Likelihood Ratio approach is adopted in order to overcome this uncertainty, resulting in different tests intimately related to familiar diversity combining techniques. The performance of the proposed detectors is analyzed and tested in different scenarios.