Spectrum sensing aims to sense the potential spectrum resources available in the cognitive radio environment. It is also the premise of spectrum management and spectrum sharing in cognitive radio systems. To perceive the primary user’s activity and make full use of spectrum holes, rapid detection of a broad frequency span is an essential part of cognitive radio technology. Reducing the observation time for the data collection, the data storage requirements, and hardware/software computational complexity are urgent and challenging issues in wideband spectrum sensing. High accuracy power spectral density estimation is not the primary requirement; of course, the accuracy must be controlled within the appropriate range and can support the primary user activity’s determination. This paper proposes a sub-Nyquist wideband spectrum sensing method based on compressive covariance sensing for the rapid wideband spectrum sensing. Compared with the traditional Nyquist-rate method, this method can use low-speed ADC to detect wideband signals and effectively control the observation time and computational complexity. This paper’s main contributions include: (1) developing a sub-Nyquist sampling structure based on the multi-coset sampling banks, (2) proposing a coarse-grained power spectral density estimation method for wideband spectrum sensing with short observation time and low complexity. Simulations show that the proposed method exhibits this method is suitable for fast spectral detection. At the same time, the error of spectrum analysis is basically within the acceptable range.

频谱感测旨在感测认知无线电环境中可用的潜在频谱资源。这也是认知无线电系统中频谱管理和频谱共享的前提。为了感知主要用户的活动并充分利用频谱空缺，快速检测宽频率范围是认知无线电技术的重要组成部分。减少数据收集的观察时间，数据存储要求以及硬件/软件计算复杂性是宽带频谱感测中迫切且具有挑战性的问题。高精度功率谱密度估计不是主要要求；当然，准确性必须控制在适当的范围内，并且可以支持主要用户活动的确定。提出了一种基于压缩协方差检测的亚奈奎斯特宽带频谱检测方法，用于快速宽带频谱检测。与传统的奈奎斯特速率方法相比，该方法可以使用低速ADC来检测宽带信号，并有效控制观察时间和计算复杂度。本文的主要贡献包括：（1）基于多陪集采样库开发亚奈奎斯特采样结构，（2）提出了一种用于观测时间短，复杂度低的宽带频谱感测的粗粒度功率谱密度估计方法。仿真表明，该方法具有良好的快速光谱检测能力。同时，频谱分析的误差基本在可接受的范围内。