This paper proposes a novel, highly effective spectrum

sensing algorithm for cognitive radio and white space

applications. The proposed spectral covariance sensing (SCS)

algorithm exploits the different statistical correlations of the received

signal and noise in the frequency domain. Test statistics are

computed the covariance matrix of a partial spectrogram

and compared with a decision threshold to determine whether a

primary signal or arbitrary type is present or not. This detector is

analyzed theoretically and verified through realistic open-source

simulations using actual digital television signals captured in the

US. Compared to the state of the art in the literature, SCS

improves sensitivity by 3 dB for the same dwell time, which is a

very significant improvement for this application. Further, it is

shown that SCS is highly robust to noise uncertainty, whereas

many other spectrum sensors are not.-This paper proposes a novel, highly effective spectrum

sensing algorithm for cognitive radio and white space

applications. The proposed spectral covariance sensing (SCS)

algorithm exploits the different statistical correlations of the received

signal and noise in the frequency domain. Test statistics are

computed　the covariance matrix of a partial spectrogram

and compared with a decision threshold to determine whether a

primary signal or arbitrary type is present or not. This detector is

analyzed theoretically and verified through realistic open-source

simulations using actual digital television signals captured in the

US. Compared to the state of the art in the literature, SCS

improves sensitivity by 3 dB for the same dwell time, which is a

very significant improvement for this application. Further, it is

shown that SCS is highly robust to noise uncertainty, whereas

many other spectrum sensors are not.