Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

认知无线电（CR）旨在实现动态频谱共享，并减少由无线设备数量的指数增长引起的频谱稀缺性的负面影响。CR要求频谱感测应快速，准确地检测许可的信号，并确保主要用户和次要用户之间共存而不会产生干扰。但是，在CR系统中，具有低信噪比（SNR）的频谱感测仍然是一个挑战。提出了一种利用随机共振和滤波器的基于协方差矩阵的频谱感知方法。SR被实现为在低SNR条件下强制执行多个天线的检测信号。接收机中装有滤波器，以减少噪声频率的干扰。然后，由似然比检验（LRT）或最大特征值检测器（MED）计算的两个检验统计量由处理后信号的样本协方差矩阵构成。仿真结果表明了所提出算法在各种信道条件下的频谱感知性能，即加性高斯白噪声（AWGN）和瑞利衰落信道。还将能量检测器（ED）与LRT和MED进行比较。仿真结果表明，在强噪声背景下，SR和滤波器的实现可以显着改善频谱感测性能。加性高斯白噪声（AWGN）和瑞利衰落信道。还将能量检测器（ED）与LRT和MED进行比较。仿真结果表明，在强噪声背景下，SR和滤波器的实现可以显着改善频谱感测性能。加性高斯白噪声（AWGN）和瑞利衰落信道。还将能量检测器（ED）与LRT和MED进行比较。仿真结果表明，在强噪声背景下，SR和滤波器的实现可以显着改善频谱感测性能。