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A new sensing algorithm based on the ratio of the difference of maximum-minimum eigenvalues to geometric mean eigenvalue of the sampled covariance matrix is proposed. The algorithm uses the logarithmic distribution characteristics of geometric mean eigenvalue, through comparing the ratio and threshold value to determine whether the primary user occupies the distribution spectrum, and the prior knowledge of the primary signal and noise are not needed, but a simple closed-form expression of threshold is obtained. The simulation results show that the proposed algorithm can get better perceived performance under the conditions of low signal to noise ratio, few collaborative users and few samples. On the other hand, it has steady sensing performance, it will be less affected by either the extreme values or the false-alarm probability.