摘要
宽带感知需要超宽的射频前端,非常快的数字处理设备,传统的频谱检测机制承受着巨大的压力。利用宽带频谱中信号频谱的稀疏特性以及实际稀疏数据观察值成组出现,即组稀疏的特性,将压缩传感算法应用于宽带频谱感知,通过阈值判断,对欠采样下的信号进行重构,进而进行频谱感知,提出了基于压缩传感的频谱协方差感知算法,当压缩比为0.1时系统的检测概率接近100%且能够正确估计出信号频谱的位置,与SCS算法相比,减少射频端的采样率,缓解了信号采集端的压力,降低了系统的成本。
Wideband sensing need very wide RF front-end,very fast digital processing equipment,the traditional spectral detection is under tremendous pressure.This paper utilize the sparse characteristics of the wideband signal spectrum and group sparsity characteristics of the observation that in practical sparse data,the compressed sensing algorithm is applied to wideband spectrum sensing,reconstruct sub-nyquist sampling signal via threshold to determine,then perform spectrum sensing,spectrum covariance sensing algorithm based on compressed sensing is proposed,systemic detection probability is close to 100% and can estimate the location of the signal spectrum correctly when the compressed ratio is 0.1,compared to SCS arithmetic,reduce the sampling rate of the RF side,ease the pressure of the signal collection side,reduce the system costs.
出处
《传感技术学报》
CAS
CSCD
北大核心
2011年第7期1022-1026,共5页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(60772079)
国家自然科学基金项目(61071200)
河北省自然科学基金项目(F2010001294)
关键词
频谱感知
压缩传感
采样率
频谱协方差
spectrum sensing
compressed sensing
sampling rate
spectrum covariance
