To avoid harmful interference to primary user system and improve the utilization of spectrum resource, it is necessary for the spectrum sensing algorithms of cognitive radio system to detect primary users' signals and identify spectrum resource available as fast as possible under very low signal-to-noise (SNR) environments. Recently, covariance matrix based spectrum detection algorithm has been proposed as an efficient blind spectrum sensing method in cognitive radio context because it can avoid the noise uncertainty problem suffered by energy detection. To further improve the spectrum sensing performance under very low SNR region, a stochastic resonance enhanced covariance matrix based spectrum sensing algorithm is proposed in this paper. By adding specific optimal signal into the received signals, the proposed covariance matrix based spectrum sensing method can enlarge the deflection of the detection statistics' probability density functions (PDF) , which are associate with primary user signal existing, or not. Then, the spectrum sensing performance can be improved under very low SNR region. Comparing with existing covariance matrix based spectrum sensing algorithm, simulation results show that the proposed spectrum sensing method can significantly improve the detection probability as well as reduce detection period for the same probability of false alarm under very low SNR region.%为了在避免对主用户系统产生有害干扰的同时提高频谱利用效率,要求认知无线电系统的频谱感知算法能在极低的信噪比下快速检测出主用户信号.由于可以避免能量检测面临的噪声不确定性问题,基于协方差矩阵的检测算法是一种有效的盲频谱感知算法.为了进一步提高极低信噪比下的性能,本文提出了一种基于随机共振的协方差矩阵频谱感知算法.该算法通过在接收信号中加入优化的特定信号,利用随机共振原理,增大有无主用户信号下的检测统计量概率分布函数的分离度,提高频谱感知的性能.仿真结果表明,相对于现有的协方差矩阵频谱感知算法,在相同的虚警概率下,所提算法可以显著提高极低信噪比下的检测概率,同时大幅度缩减检测时间.
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